Ant colony System_A Cooperative Learning Approach to the TSP

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大学英语技能培训阅读部分练习题

Part Ⅱ Reading Comprehension (35 minutes)Passage OneQuestions 21 to 25 are based on the following passage.Antarctica has actually become a kind of space station - a unique observation post for detecting important changes in the world’s environment. Remote from major sources of pollution and the complex geological and ecological systems that prevail elsewhere, Antarctica makes possible scientific measurements that are often sharper and easier to interpret than those made in other parts of the world.Growing numbers of scientists therefore see Antarctica as a distant early warning sensor, where potentially dangerous global trends may be spotted before they show up to the north. One promising field of investigation is glaciology. Scholars from the United States, Switzerland, and France are pursuing seven separate but related projects that reflect their concern for the health of the West Antarctic Ice Sheet - a concern they believe the world at large should share.The Transantarctic Mountain, some of them more than 14,000 feet high, divide the continent into two very different regions. The part of the continent to the “east” of the mountains is a high plateau covered by an ice sheet nearly two miles thick. “West” of the mountain, the half of the continent south of the Americas is also covered by an ice sheet, but there the ice rests on rock that is mostly well below sea level. If the West Antarctic Ice Sheet disappeared, the western part of the continent would be reduced to a sparse cluster of island.While ice and snow are obviously central to many environmental experiments, others focus on the mysterious “dry valley” of Antarctica, valleys that contain little ice or snow even in the depths of winter. Slashed through the mountains of southern Victoria land, these valleys once held enormous glaciers that descend 9,000 feet from the polar plateau to the Ross Sea. Now the glaciers are gone, perhaps a casualty of the global warming trend during the 10,000 years since the ice age. Even the snow that falls in the dry valleys is blasted out by vicious winds that roar down from the polar plateau to the sea. Left bare are spectacular gorges, rippled fields of sand dunes, clusters of boulders (大圆石) sculptured into fantastic shapes by 100-mile an hour winds, and an aura of extraterrestrial desolation.Despite the unearthly aspect of the dry valleys, some scientists believe that they may carry a message of hope for the verdant (草木繁茂的) parts of the earth. Some scientists believe that in some cases the dry valleys may soak up pollutants faster than pollutants enter them.21. Antarctica is scientifically important in that ____.A) it is a space station B) it is an ideal place for the investigation of glaciologyC) there is the mysterious dry valleyD) it can help people detect global environmental changes22. The reason for the disappearance of glaciers in the dry valley is ____.A) that they’ve descended to the Ross seaB) that they’ve been blasted out by vicious windsC) the global warming trend ever since the ice ageD) that they’ve been changed into gorges, sand dunes and boulders23. When the author calls Antarctica “distant early warning sensor”, he actually means that ____.A) such equipment has been set up for scientific purposeB) the research groups there are like such kind of sensorsC) potential global changes can be seen on Antarctica firstD) Antarctica is remote from other parts of the earth24. Which of the following statements is true according to the article?A) There is a cluster of island west of the Transantartic Mountain.B) Scientific research on Antarctica only centers on the ice and snow there.C) Dry valleys may be a place to dispose of our pollutants.D) All the countries on earth should be concerned about the health of the West Antarctic Ice Sheet.25. The word “spotted” (2nd paragraph) can best be replaced by____.A) placed B) noticed C) fixed D) judgedPassage TwoQuestions 26 to 30 are based on the following passage.The Carnegie Foundation report says that many colleges have tried to be “all things to all people”. In doing so, they have increasingly catered to a narrow minded careerism while failing to cultivate a global vision among their students. The current crisis, it contends, does not derive from a legitimate desire to put learning to productive ends. The problem is that in too many academic fields, the work has no context; skills, rather than being means, have become ends. Students are offered a variety of options and allowed to pick their way to a degree. In short, driven by careerism, “the nation’s colleges and universities are more successful in providing credentials (文凭) than in providing a quality education for their students.” The report concludes that the special challenge confronting the undergraduate college is one of shaping an “integrated core” of common learning. Such a core would introduce s tudents “to essential knowledge, to connections across the disciplines, and in the end, toapplication of knowledge to life beyond the campus.”Although the key to a good college is a high quality faculty, the Carnegie study found that most colleges do very little to encourage good teaching. In fact, they do much to undermine it. As one professor observed: “Teaching is important, we are told, and yet faculty know that research and publication matter most.” Not surprisingly, over the last twenty years c olleges and universities have failed to graduate half of their four-year degree candidates. Faculty members who dedicate themselves to teaching soon discover that they will not be granted tenure (终身任期), promotion, or substantial salary increases. Yet 70 percent of all faculty say their interests lie more in teaching than in research.Additionally, a frequent complaint among young scholars is that “There is pressure to publish, although there is virtually no interest among administrators or colleagues in the content of the publications.”26. When a college tries to be “all things to all people” (line 1, Para. I), it aims to ____.A) satisfy the needs of all kinds of students simultaneouslyB) focus on training students in various skillsC) encourage all sorts of people to attend collegeD) make learning serve academic rather than productive ends27. The word “core” (line 8, 1st paragraph) might mean____.A) aim B) unit C) center D) course28. One of the reasons for the current crisis in American colleges and universities is that ____.A) a narrow vocationalism has come to dominate many collegesB) students don’t have enough freedom in choosing what they want to learnC) skills are being taught as a means to an endD) students are not interested in learning29. American colleges and universities failed to graduate half of their four year degree candidates because ____.A) most of them lack high quality facultiesB) students are becoming more and more lazyC) there are not enough incentives for students to study hardD) they attach greater importance to research and publication than to teaching30. It can be inferred from the passage that high quality college education calls for ____A) high quality facultiesB) a commitment to students and effective teachingC) the cultivation of students’ interest in learningD) dedication to research in frontier areas of knowledgePassage ThreeQuestions 31 to 35 are based on the following passage.The U.S. birthrate began to decline in the middle 1950’s, resulting in a smaller college age population starting in the middle 1970’s.S4 Something else happened in the 1970’s: the price of oil increased tremendously, driving up the price of almost everything and making Americans aware that their large automobiles used a lot of gasoline. At the same time, foreign car manufacturers had begun to produce small fuel efficient cars in large quantities for the export market. Suddenly, the large, gas guzzling American cars were no longer attractive to American buyers, who began buying foreign cars by the thousands. The American automobile industry went into a recession.Thousands of automotive workers were laid off, as were thousands of people in industries indirectly connected with the auto industry. People who are laid off tend to keep what money they have for necessities, like food and housing. They do not have the extra money needed to send their children to college. Their children cannot pay their own college costs, because during a recession they cannot find jobs. High unemployment means that more state funds must be used for social service - unemployment benefits and to aid dependent children, for example - than during more prosperous times. It also means that the states have fewer funds than usual, because people are paying fewer taxes. Institutions of higher education depend on two major sources of income to keep them functioning: tuition from students and funds from the states. At the present time, there are fewer students than in the past and fewer state funds available for higher education. The colleges and universities are in trouble.31. What is the main idea of this passage?A) The rising of oil price drove up the price of everything.B) There were many reasons why higher education was in trouble in the 1970’s.C) Birthrate began to decline in the USA in 1950’s.D) High unemployment caused a lot of social problems.32. The phrase “laid off” can best be replaced by which of the following?A) Poor . B) Got rid of. C) Removed. D) Unemployed.33. American cars were not popular in their domestic markets because they were____.A) small B) gas consuming C) fuel efficient D) not attractive34. The colleges and universities were in trouble because of the following reasons except that ____.A) they couldn’t get enough income to keep them runningB) young people couldn’t afford the tuition feesC) keeping them running at the same level would cost much moreD) social services need more state funds because of the recession35. All of the following statements are true EXCEPT ____.A) young people couldn’t afford their own tuition in the 1970’sB) it’s difficult for graduates from colleges to find a job in the 1970’sC) fewer parents could afford to send their children to college because of the recession in 1970’sD) Birthrate dropped in the 1970’s because of the recessionPassage FourQuestions 36 to 40 are based on the following passage.Within fifteen years Britain and other nations should be well on with the building of huge industrial complexes for the recycling of waste. The word rubbish could lose its meaning because everything that goes into the dumps would be made into something useful. Even the most dangerous and unpleasant wastes would provide energy if nothing else.The latest project is to take a city of around half a million inhabitants and discover exactly what raw materials go into it and what go out. The aim is to find out how much of these raw materials could be provided if a plant for recycling waste were built just outside the city. This plant would recycle not only metal such as steel, lead and copper, but also paper and rubber as well.Another new project is being set up to discover the best ways of sorting and separating the rubbish. When this project is complete, the rubbish will be processed like this: first, it will pass through sharp metal bars which will tear open the plastic bags in which rubbish is usually packed; then it will pass through a powerful fan to separate the lightest elements from the heavy solids; after that grounders and rollers break up everything that can be broken. Finally the rubbish will pass under magnets, which will remove the bits of iron and steel; the rubber and plastic will then be sorted out in the final stage. The first full-scale giant recycling plants are, perhaps, fifteen years away. Indeed, with the growing cost of transporting rubbish to more distant dumps, some big cities will be forced to build their own recycling plants before long.36. The main purpose of the passage is ____.A) to show us a future way of recycling wastesB) to tell the importance of recycling wastesC) to warn people the danger of some wastesD) to introduce a new recycling plant37. How many stages are there in the recycling process?A) 3. B) 4. C) 5. D) 6.38. What is the main reason for big cities to build their own recycling plants?A) To deal with wastes in a better way.B) It’s a good way to gain profits.C) It’s more economical than to dump wastes in some distant places.D) Energy can be got at a lower price.39. The first full-scale huge recycling plants ____.A) have been in existence for 15 years B) takes 15 years to buildC) can’t be built until 15 years later D) will remain functioning for 15 years40. Which of the following statements is true?A) The word “rubbish” will soon disappear from dictionaries.B) Dangerous wastes can be recycled into nothing but energy.C) To recycle paper and rubber will still be impossible even with the new recycling methods.D) Big cities will soon have their own recycling plants.Part Ⅲ Vocabulary and Structure (20 minutes)41. Yo u ____ her in office last Friday; She’s been out of town for two weeks.A) needn’t have seen B) might have seenC) must have seen D) can’t have seen42. This candidate has far more chances of winning the election than ____ recommended by the organizer.A) that B) the one C) whom D) one43.____ difficult it is to surmount the obstacles, we’re bound to achieve our goal.A) As B) How C) So D) However44. Many a time ____ not to play with fire but he turns a deaf ear to the warnings.A) the child being told B) the child has been toldC) has been told the child D) has the child been told45. He ____ writing the paper now. He hadn’t written a single word when I left him ten minutes ago.A) shouldn’t be B) can’t have finishedC) can’t be D) mustn’t have finished46. Isn’t it lovely to think that I ____ myself on the sunny beach tomorrow at this time.A) will enjoy B) am enjoying C) will be enjoying D) shall enjoy47. Don’t you know it’s the first time he ____ this kind of meeting?A) attends B) attended C) has attended D) is attending48. If you ____ my advice, you ____ your failure now. You ____ your victory.A) took ... wouldn’t cry over ... would celebrateB) had taken ... wouldn’t have cried over ... would have celebratedC) had taken ... aren’t crying over ... are celebratingD) had taken ... wouldn’t be crying over ... would be celebrating49. I would rather ____ out to look for a job instead of moping around here everyday.A) to go B) going C) went D) go50. - I must have eaten something wrong. I feel like ____.- I told you not to eat at a restaurant. You’d better ____ at home.A) to throw up ... to eat B) throwing up ... eatingC) to throw up ... eat D) throwing up ... eat51. He always dreams of ____ a chance for him to bring into full play his potential.A) there being B) there to be C) there is D) being52. You should keep an eye ____ the slightest changes in the patient while the doctor is away.A) for B) on C) to D) about53.____ is still a controversial issue.A) If he is the right person for the job B) That he is the right person for the jobC) Whether he is the right person for the job D) He is the right person for the job54. He has won the first place, ____ is clear from the expressions on his face.A) that B) as C) what D) when55. His response was ____ that he didn’t say yes and he didn’t say no.A) so B) what C) what D) such56. It’s time f or us to ____ the traditional Chinese architecture.A) preserve B) reserve C) conserve D) deserve57. I’m afraid taking a part-time job might ____ my time for study.A) cut off B) cut into C) cut down D) cut away58. The innocent young man was ____ of robbing the bank.A) sentenced B) charged C) accused D) punished 59. People should behave ____ on such a solemn occasion.A) respectedly B) respectfully C) respectingly D) respectively60. After second thought, she ____ a better solution.A) came up with B) added up to C) put up with D) made up for61. I didn’t ____ to tell him the truth. He forced me into doing that.A) expect B) suppose C) hope D) mean62. If this kind of animal becomes ____, our future generation won’t even have a chance to see it.A) little B) scarce C) rare D) short63. Little kids are OK most of the time. But sometimes can become a real ____.A) difficulty B) nuisance C) worry D) anxiety64. Early settlers in this land found great difficulty in ____ to the harsh living conditions.A) adopting B) fitting C) settling D) adapting65. Cultural exchanges between the two countries help to ____ understanding and friendship between the two peoples.A) increase B) raise C) promote D) quicken66. His downfall is ____ to other factors than this.A) contributable B) attributable C) deducible D) responsible67. I think you should go to see a doctor, who may ____ to you proper medicine so that you can recover faster.A) prescribe B) subscribe C) submit D) prohibit68. It pains us to see that our environment is ____.A) degenerating B) deteriorating C) declining D) depressing69. The age of the students in this class ____ from eighteen to twenty.A) changes B) alters C) ranges D) limits70. After finishing the paper, he ____ himself to have a good rest.A) extended B) stretched C) spread D) reachedPart Ⅳ (omitted)Part Ⅴ Writing (30 minutes)Keys:21.D 22.C 23.C 24.D 25.B 26.B 27.C 28.A 29.D 30.B 31.B 32.D 33.B 34.C 35.D 36.A 37.B 38.C 39.C 40.D 41.D 42.B 43.D 44.D 45.B 46.C 47.C 48.D 49.D 50.D 51.A 52.B 53.C 54.B 55.D 56.A 57.B 58.C 59.B 60.A 61.D 62.B 63.B 64.D 65.C 66.B 67.A 68.B 69.C 70.B。

合作学习与协作学习概念辨析：collaborative-learning-versus-cooper

合作学习与协作学习概念辨析：collaborative learning versuscooperative learningA Definition of Collaborative vs Cooperative Learning Ted Panitz (1996)I have been searching for many years for the Holy Grail of interactive learning, a distinction between collaborative and cooperative learning definitions. I am getting closer to my elusive goal all the time but I am still not completely satisfied with my perception of the two concepts. I believe my confusion arises when I look at processes associated with each concept and see some overlap or inter-concept usage. I will make a humble attempt to clarify this question by presenting my definitions and reviewing those of other authors who have helped clarify my thinking.Collaboration is a philosophy of interaction and personal lifestyle whereas cooperation is a structure of interaction designed to facilitate the accomplishment of an end product or goal.Collaborative learning (CL) is a personal philosophy, not just a classroom technique. In all situations where people come together in groups, it suggests a way of dealing with people which respects and highlights individual group members' abilities and contributions. There is a sharing of authority and acceptance of responsibility among group members for the groups actions. The underlying premise of collaborative learning is based upon consensus building through cooperation by group members, in contrast to competition in which individuals best other group members. CL practitioners apply this philosophy in the classroom, at committee meetings, with community groups, within their families and generally as a way of living with and dealing with other people.Cooperative learning is defined by a set of processes which help people interact together in order to accomplish a specific goal or develop an end product which is usually content specific. It is more directive than a collaboratve system of governance and closely controlled by the teacher. While there are many mechanisms for group analysis and introspection the fundamental approach is teacher centered whereas collaborative learning is more student centered.Spencer Kagan in an article in Educational Leadership (Dec/Jan 1989/1990) provides an excellent definition of cooperative learning by looking at general structures which can be applied to any situation. His definition provides an unbrella for the work cooperative learning specialists including the Johnsons, Slavin, Cooper, Graves and Graves, Millis, etc. It follows below:"The structural approach to cooperative learning is based on the creation, analysis and systematic application of structures, or content-free ways of organizing social interaction in the classroom. Structures usually involve a series of steps, with proscribed behavior at each step. An important cornerstone of the approach is the distinction between "structures" and "activities"."To illustrate, teachers can design many excellent cooperative activities, such as making a team mural or a quilt. Such activities almost always have a specific content-bound objective and thus cannot be used to deliver a range of academic content. Structures may be used repeatedly with almost any subjectmatter, at a wide range of grade levels and at various points in a lesson plan."John Myers (Cooperative Learning vol 11 #4 July 1991) points out that the dictionary definitions of "collaboration", derived from its Latin root, focus on the process of working together; the root word for "cooperation" stresses the product of such work. Co-operative learning has largely American roots from the philosophical writings of John Dewey stressing the social nature of learning and the work on group dynamics by Kurt Lewin. Collaborative learning has British roots, based on the work of English teachers exploring ways to help students respond to literature by taking a more active role in their own learning. The cooperative learning tradition tends to use quantitative methods which look at achievement: i.e., the product of learning. The collaborative tradition takes a more qualitative approach, analyzing student talk in response to a piece of literature or a primary source in history. Myers points out some differences between the two concepts:"Supporters of co-operative learning tend to be moreteacher-centered, for example when forming heterogeneous groups, structuring positive inter- dependence, and teachingco-operative skills. Collaborative learning advocates distrust structure and allow students more say if forming friendhip and interest groups. Student talk is stressed as a means for working things out. Discovery and contextural approaches are used to teach interpersonal skills.""Such differences can lead to disagreements.... I contend the dispute is not about research, but more about the morality of what should happen in the schools. Beliefs as to whast should happen in the schools can be viewed as a continuum of orientations toward curriculum from "transmission" to "transaction" to "transmission". At one end is the transmission position. As the name suggests, the aim of this orientation is to transmit knowledge to students in the form of facts, skills and values. The transformation position at the other end of the continuum stresses personal and social change in which the person is said to be interrelated with the environment rather than having control over it. The aim of this orientation isself-actualization, personal or organizational change."Rocky Rockwood (National Teaching and Learning Forum vol 4 #6, 1995 part 1) describes the differences by acknowledging the parallels they both have in that they both use groups, both assign specific tasks, and both have the groups share and compare their procedures and conclusions in plenary class sessions. The major difference lies in the fact that cooperative deals exclusively with traditional (canonical) knowledge while collaborative ties into the social constructivist movement, asserting that both knowledge and authority of knowledge have changed dramatically in the last century. "The result has been a transition from "foundational (cognitive) understanding of knowledge", to a nonfoundational ground where "we understand knowledge to be a social construct and learning a social process" (Brufee, Collaborative learning: Higher Education, Interdependence, and the Authority of Knowledge, 1993). Rockwood states:"In the ideal collaborative environment, the authority for testing and determining the appropriateness of the group product rests with, first, the small group, second, the plenary group (the whole class) and finally (but always understood to be subject to challenge and revision) the requisite knowledge community (i.e.the discipline: geography, history, biology etc.) The concept of non- foundational knowledge challenges not only the product acquired, but also the process employed in the acquisition of foundational knowledge.""Most importantly, in cooperative, the authority remains with the instructor, who retains ownership of the task, which involves either a closed or a closable (that is to say foundational) problem ( the instructor knows or can predict the answer). In collaborative, the instructor--once the task is set-- transfers all authority to the group.In the ideal, the group's task is always open ended.""Seen from this perspective, cooperative does not empower students. It employs them to serve the instructor's ends and produces a "right" or acceptable answer. Collaborative does truly empower and braves all the risks of empowerment (for example, having the group or class agree to an embarrassingly simplistic or unconvincing position or produce a solution in conflict with the instructor's).""Every person, Brufee holds, belongs to several "interpretativeor knowledge communities" that share vocabularies, points of view, histories, values, conventions and interests. The job of the instructor id to help students learn to negotiate the boundaries between the communities they already belong to and the community represented by the teacher's academic discipline, which the students want to join. Every knowledge community has a core of foundational knowledge that its members consider as given (but not necessarily absolute). To function independently within a knowledge community, the fledgling scholar must master enough material to become conversant with the community."Rockwood concludes:"In my teaching experience, cooperative represents the best means to approach mastery of foundational knowledge. Once students become reasonably conversant, they are ready for collaborative, ready to discuss and assess,...."Myers suggests use of the "transaction" orientation as a compromise between taking hard positions advocating either methodology."This orientation views education as a dialogue between the student and the curriculum. Students are viewed as problem solvers. Problem solving and inquiry approaches stressing cognitive skills and the ideas of Vygotsky, Piaget, Kohlberg and Bruner are linked to transaction. This perspective views teaching as a "conversation" in which teachers and students learn together through a process of negotiation with the curriculum to develop a shared view of the world."It is clear to me that in undertaking the exercize of defining differences between the two ideas we run the risk of polarizing the educational community into a we versus them mentality. There are so many benefits which acrue from both ideas that it would be a shame to lose any advantage gained from the student-student-teacher interactions created by both methods. We must be careful to avoid a one-size-fits-all mentality when it comes to education paradigms.As a final thought, I think it behooves teachers to educate themselves about the myriad of techniques and philosophies which create interactive environments where students take moreresponsibility for their own learning and that of their peers. Then it will become possible to pick and chose those methods which best fit a particular educational goal or community of learners.现代汉语词典中：合作：互相配合做某事或共同完成某项任务。

Experiencing the NHS

INTRODUCTIONChina is now at a critical stage in its health care reforms. Learn-ing successful experiences from other countries is important to us. Among various health care systems, the NHS in the United Kingdom has shown its global reputation for serving the public with free health care of high quality and equal-ity. To gain vivid knowledge from the NHS system, 25 senior doctors from China were sent as clinical observers to different hospitals for 3 months under joint support from the Chinese Government and the UK Severn Deanery. We represent various disciplines, including urol-ogy, general surgery, neuro-surgery, orthopedics, endocrinology, gastro-enterology and anesthesiology.EXPERIENCE IN BRISTOLThe authors were appointed to work in the Departments of Gas-troenterology and Neuro-Surgery at Frenchay Hospital, North Bristol NHS Trust. Under the guidance and with the warmhearted help of con-sultants and registrars, we partici-pated in outpatient clinics, inpatientXi Jin (1) and Liang Wen (2)1. Department of Digestive Disease and 2. Department of Neuron Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang UniversityExperiencing the NHS systemA three month visit by Chinese clinical observers wards round, surgery, endoscopy, ERCP, academic meeting and other activities. Although it is difficult to learn special techniques during such a short time, we are impressed by the working environment, NHS sys-tem and doctor-patient relationships MEDICAL EDUCATION AND TRAINING We found the medical education and training system here is excel-lent. I was told that a registrar would have about eight years train-ing in specialty with a fair salary from the UK government. More importantly, the majority of con-sultants have a strong teaching awareness and are good at teach-ing. There are also a lot of training courses for special techniques. Be-sides, the patients here are very co-operative. They do not refuse medi-cal activity carried out by registrars instead of consultants, which guar-antees that the registrar would be a competent specialist after training. Furthermore, the training system is quite flexible for individuals, as you can apply for a period of leave for research or fellow work in another country. Impressively, the name “theatre” is full of imagination and the operation here is truly an art. The employee supportive system is another important experience we can learn. It is quite normal and frequent that doctors or nurses have professional and personal problems during busy clinical work, especial-ly when some employees are young and sensitive. Sometimes it is dif-ficult to solve these problems alone and hence it is important to let them know where and how they can get help. WORKING ENvIRONMENT AND DOCTOR-PATIENT RELATIONSHIP The working environment and doctor-patient relationship is really harmonious in the UK. The outpa-tient clinics are not crowded and every patient generally gets suffi -cient time to discuss their concerns with doctors. The doctors are all very friendly to patients. The consultationWEMJ v olume 112 No 2 Article 1 June 2013ADDRESS FOR CORRESPONDENCE:No.79 Qing Chun Road, Hangzhou, Zhejiang Province, China, PO 310003Email: jxfl007@ Dr Jin Xi (Zhejiang University) and Dr. Sanjay Gandhi (Frenchay Hospital, Bristol)usually starts by the doctor intro-ducing themselves and addressing the patient with the title “Sir” or “Madam” and they generally end the consultation with the sentence “any questions”?The patients are really thankful and trust in doctors and the doc-tors do their best to help patients, not only in treating disease and alleviating sufferings, but also in creating every possible convenience for patients. The patient privacy is always put in the first line. Every time when I was present for clini-cal activities, the consultant asked patients for their permission, which showed respect for patients’ pri-vacy.GENERAL PRACTICEWe believe that the General Prac-tice (GP) system is very successful in acting as the “gate keeper” for the NHS system, where GPs pro-vide basic medical service to local patients as well as decide and ar-range the appointment of patients with special clinics. This system is really helpful in reducing medi-cal expense and keeping equality. In China, patients always go to the hospitals without appointment and they believe that the larger hospi-tal, the higher the medical service quality. Hospitals in China are gen-erally overcrowded, which causespatient’s dissatisfaction towardsdoctors and a tense bilateral rela-tionship. For instance, the hospitalI work in is the biggest one in Zhe-jiang province and has over 3000beds. However, the beds are alwaysin shortage and when I am in GIoutpatient clinics I have to see overone hundred patients a day. I amsure you can imagine how stressedI become and how hurried the pa-tients feel! Therefore, after witness-ing the harmony and equality in theNHS system and the doctor-patientrelationship, we would say thatChinese Government could learn toset up a GP system and send medi-cal managers to the NHS for betterunderstanding.TWO SIDES OF THE COIN?As every coin has two sides, wewould like to mention that there area few aspects that could further im-prove the NHS. The most importantone we feel is that the efficiencyin non-emergency cases should beenhanced. The GP system has great-ly reduced the medical expense as“Gate keeper” but it takes a con-siderable time to transfer patientsto the specialists and get specialinvestigations. Under the currentsystem, for instance, patients mayneed months to get a GI endoscopy.Similar delays can be seen in otherspecialties, which may delay dis-ease diagnosis.FUNDINGOur visit was sponsored by theChina (Zhejiang)-UK (SevernDeanery) Clinical Exchange Pro-gram.ACKNOWLEDGEMENTSWe would like to thank the Deanof our hospital and the Directors ofour departments for their supportin this visit. We would also like tothank Mr. Chen Zheng-fang andhis colleagues in the Health Bureauof Zhejaing province, China andProfessor Davinder P S Sandhu andhis colleagues in the Severn Dean-ery, UK for their arrangement andhelp in this project. We are verygrateful for Dr. M Lockett, Dr. RPrzemioslo and their colleaguesin the GI department and Dr. Ni-tin Patel and his colleagues in theNeuro-Surgery department for theirwarmhearted help in the clinics andlife. Many thanks go to Dr SanjayGandhi for his invitation on behalfof the West of England MedicalJournal to write this article and hishelp and suggestions with regard tothe manuscript.Experiencing the NHS system (continued) WEMJ v olume 112 No 2 Article 1 March 2013。

美国合作语言学(Cooperative Language Learning Approch)内容浅析

中图分类号：３．１文献标识码：文章编号：０９０ｏ２０１ — ０５０Ｇ６３４Ａ１０ — １ｘ（０８）００４ — ３
一
、
合作语言学习教学法概述
他已经掌握了所学知识的重点，以避免任务只落在小组中
个别成员头上。因此，教师可以进行个人测试，或随机抽取学生进行个别测试。３ｒｐＩｔｒｔｕ小组交流）小组成员应进行面对．ｏｅａｉ（Ｇｕｕｒｃｏ，
上，竞争” 合作 ”为解决一个共同的问题，化“ 为“ ，完成一个
赖、它是合作学习的核心。Ｉｏ学习者必须时刻牢记小组的利益，习惯于考虑“ ” 我们而不是“ 。我”组员之间互利互助，共同进步。老师可以通过设立共同目标、共同奖励、分享材料、．分配任务等方式来实现正相互依赖。
文科教学探索
美国合作语言掌习（ｏｅａｉｅＬｎｕｇＣｏｐｒｔａｇａｅｖ
Ｌａｎｎｐｏｃ内容浅析ｅｒｉｇＡｐｒａｈ）
陈红
１０８）０８０（中国政法大学，北京
摘要：合作语言学习
在一个传统的课堂上，学生处于竞争机制之中。“ 优
等” 成绩是与“ 劣等 ” 成绩相比之下而存在的，一个学生的
我国古典教育名著《学记》说：曾 “ 独立而无友，则孤陋而寡
闻，”
成功需要其他学生的失败作为代价。美国的一些心理学家
把这种情况Ⅱ做ＮｇｔｅＩｅｅｅｄｎｅ（ｑｅａｖｔｄｐｎｅｃ具有负面意义ｉｎｒ
绝：大部分是集体合作的结果，有跨国的，有跨洲的。英语是

托福100句背7000单词

100句背7000单词1. Typical of the grassland dwellers of the continent is the American antelope, or pronghorn.美洲羚羊，或称叉角羚，是该大陆典型的草原动物。

2. Of the millions who saw Haley's comet in 1986, how many people will live long enough to see it return in the twenty-first century? 1986年看见哈雷慧星的千百万人当中，有多少人能够长寿到足以目睹它在二十一世纪的回归呢？3. Anthropologists have discovered that fear, happiness, sadness, and surprise are universally reflected in facial expressions.人类学家们已经发现，恐惧，快乐，悲伤和惊奇都会行之于色，这在全人类是共通的。

4. Because of its irritating effect on humans, the use of phenol as a general antiseptic has been largely discontinued.由于苯酚对人体带有刺激性作用，它基本上已不再被当作常用的防腐剂了。

5. In group to remain in existence, a profit-making organization must, in the long run, produce something consumers consider useful or desirable.任何盈利组织若要生存，最终都必须生产出消费者可用或需要的产品。

6. The greater the population there is in a locality; the greater theneed there is for water, transportation, and disposal of refuse.一个地方的人口越多，其对水，交通和垃圾处理的需求就会越大。

给一所私立学校的建议书（Aproposalforaprivateschool）

给一所私立学校的建议书（A proposal for a private school）2006-07-10 17:28:22 reprinted a label for a private school: characteristic school classification: Education MarketizationHere's my advice to a private school, and I'd like to hear your opinion....... Your recruitment staff, now the main job is to recruit students. I think in addition to enrollment, you may also be thinking about the way out of school, or how to build a prestigious school.So I asked all sorts of advice and talked about my ideas here. 1, about enrollment,2, about education and teaching,3, about school management,First, about enrollmentLet the children walk in the forefront of the times!In the short term, if we want to make a breakthrough in the enrollment work, we must make a breakthrough in the train of thought, and be able to show it well to the society. But how to look at the idea of running schools, but also to see the current social views and expectations of education, to do articles here.From the whole education environment, the problem now is not the lack of educational resources, is not a shortage of funds, lack of attention but not the education department and the society, but in all my heart without a yardstick, not a big picture of education "". Parents send their children to school, and they don't know what will happen in the future, so they can't form a good and stable expectation. We all feel that the needs of college graduates and enterprises and society is not on the number, one side is that students can not find internships and employment units, while the enterprise can not find the right talent. Even if the children graduated from Peking University and Tsinghua University, whether they can go smoothly to the society and what will happen in the future is unclear. So the most intuitive and the most prominent feeling is that education is divorced from society, very serious.And we analyze it, disconnection is reflected in many aspects, and how international issues (such as emphasis on foreign language teaching, intercultural communication course, etc.) how and network technology convergence problems (IT education, blog can highlight the practical writing course, etc.) and how the market's problems (can the design of pre Vocational education, curriculum and so on, MBA etc.) and each aspect has the connotation of different levels, different articles can do. To lead the market and consolidate the development, we need to grasp the current situation and the general trend, with the help of hot spots to complete the construction of the school's own system.From the market point of view, the foreign language teaching is hot in recent years, and has formed a certain climate. I havea look. There are many foreign language schools in Chongqing. There's nothing wrong with playing a foreign language in private schools, but it's obviously not enough to rely on this card. "Bilingual School" emphasizes that it is difficult to distinguish the message from the society, and it is not easy to break through quickly. I think the school can change the point of view, or dig out other potential hot spots to do articles.My suggestion is that you can try to connect with the market from the point of view, from the "pre service education", "small MBA course" and so on to start the article.Specific practices, such as starting from "pre service education", you can organize a working group, "take children to visit all kinds of enterprises", this kind of activities to make TV programs. In the process of visits and exchanges, can fully display the "what are busy in all walks of life, and their knowledge on what support organization system of the society and our knowledge system, and outlines the relationship between industry and enterprise, occupation, discipline, and at the same time also display a" let our children walk in front of the school, teachers and students to study here and what life is like an attractive blueprint, of course this also help some excellent enterprises for advertising. CCTV and provincial TV stations now opened a lot of youth section, but good program resources are scarce, through this program, not only can the school quickly establish a new image, can have many benefits.In addition, the enrollment and school publicity work needs to be further reflected through daily and systematic work,For example, parents can take some of the current meeting will change to introduce their work and life with children please parents, volunteers and other experiences, regular communication or "design education briefing" form and parents and society, or some public schools or schools in the rural areas of networking, etc., a comprehensive display of the society a walk in the forefront of the times and the school's healthy image.At the same time, we should pay attention to the changes in the thinking of enrollment work, may also require or will drive education, teaching and school management changes in the whole idea.[note] there are some simple analyses about the "pre service education" for teenagers, in the article "education reform: to promote the development of pre service education"(/u/470600c70100024l).Two, about education and teachingSimilar to the management of enterprises, we should steadily improve the quality of education and teaching, and provide support for the school chain expansion, it is necessary to analyze the basic elements and processes of education and teaching, as well as standardization issues.When analyzing the basic elements and processes of education and teaching, there are several relationships that need attention:One is the teaching content includes aspects, namely social all walks of life in what to do and what these things are built on the basis of knowledge, that is to say we need to understand the social and knowledge system, establish the necessary connection between the two. At present, our inquiry learning, "learning by doing" and other reforms are explored from this point of view.Two is the process of education and teaching, in essence, is the process of mutual communication between experts, teachers and students. With the development of network television, and "fool" books, between experts and students communicate more directly, the position of teachers should be familiar with the change of subject system from the corresponding selling books of knowledge to help students. At the same time, students are not only the object of education, but also may become the contributor of education.Three, the single classroom form in the past education and teaching has been replaced by online courses, TV programs, books, newspapers, interviews, interviews and other forms. That is to say, the process of modern education, teachers are no longer simply teach the textbook knowledge, but to organize all kinds of education resources, to create an independent learning, cooperative learning atmosphere for students.From the above analysis, we can see that for the standardization of modern education and teaching, there are also several layers of thinking:One is based on the understanding of teaching two aspects, put forward the new classification organization of information and knowledge, provide new cognitive guidance for teachers and students, such as outstanding scientific (and Engineering), business (and economic), (Law and Politics), culture (and entertainment) and other knowledge blocks and in social interactions, so that teachers and students can make various pieces of knowledge more effectively combined, see the full image interaction between education and society, and promote the concept of teachers personal overall transformation and all-round development of students. (that is, the general guiding ideology of running schools)The two is to be in between the teachers and students, between schools and parents, schools and society to establish a new relationship between, and make us in the teachers' training and management, the relationship between the school and the parents, schools and social interaction and so on to establish some new principles, comprehensively enhance the quality and level of the school and the social interaction. (how to manage by market orientation)?Three, teachers and students should be taken as the diggers, organizers and developers of various educational resources. In fact, not only some well-known university has a research and development and innovation ability, school innovation and production capacity is amazing, and teachers and students in this practice and innovation can be fully developed. (that is, how to implement internal management)Three, about school managementThe management of schools is often restricted by traditional ideas. Everyone thinks that the main task of students is to learn, the main task of the school is to teach. In fact,The school is a vibrant "educational science and Technology Park", with unlimited output potential. For example, to rely on the school, in youth TV programs, network education channel, youth books and other fields, in turn, the development of these areas, and will promote the rapid development of the school.We say that education is not equal to doing school, there is a large market outside the school, at least there are several aspects of concern:Teen TV show;At present, most provinces and cities television stations opened a youth column, and the corresponding program and program production team have just started, there is a lot of room for cooperation, CCTV and all provinces and cities TV station is such a situation.About the idea of the program, I'll give you an example, you may understand:In a remote rural middle school, how should 170 thousand funds be used? /u/470600c7010001egNetwork education channel;Before electronic commerce is not up, most of the websites including Sina, Baidu and other mainly rely on advertising revenue to maintain the operation, which is the core content of the channel of these sites in the new round of competition, such as Sina, the Sohu now do blog, Baidu encyclopedia are working on this. But it can be noted that, whether it is Sina, Sohu or Baidu, to support a group of professional educators is not realistic. The mode of education channel is bound to cooperate with professional organizations. And private schools have great potential competitiveness in this respect.For example, the "Encyclopedia of education" that everyone has been talking about, for now, is more likely to break through in this respect.Teenage bookshelf;The book business is to see what the bookstore will choose the books, but including Bertelsmann, I study in the library and bookstore chain have not established a decent "expert review system", which has a large space for private schools.In addition, everyone knows, to do the best education, should attract the best talent for education. But I think that only by letting people see more and better ways in education and related fields and creating a broader space for talents, can we attract talents. On this basis, the overall planning of the school and the group can be achieved.[note] I also had a discussion with a friend in Guizhou on the issue of attracting talented people:The world is flat: can the West introduce talents from the east? /u/470600c7010002z7These are just a few simple ideas, and I'd like to hear your opinions first.。

Ant Colony Optimization

Ant Colony Optimizationwith Immigrants Schemesfor the Dynamic Vehicle Routing ProblemMichalis Mavrovouniotis1and Shengxiang Yang21Department of Computer Science,University of LeicesterUniversity Road,Leicester LE17RH,United Kingdommm251@2Department of Information Systems and Computing,Brunel UniversityUxbridge,Middlesex UB83PH,United Kingdomshengxiang.yang@Abstract.Ant colony optimization(ACO)algorithms have proved tobe able to adapt to dynamic optimization problems(DOPs)when theyare enhanced to maintain diversity and transfer knowledge.Several ap-proaches have been integrated with ACO to improve its performancefor DOPs.Among these integrations,the ACO algorithm with immi-grants schemes has shown good results on the dynamic travelling sales-man problem.In this paper,we investigate ACO algorithms to solve amore realistic DOP,the dynamic vehicle routing problem(DVRP)withtraﬃc factors.Random immigrants and elitism-based immigrants are ap-plied to ACO algorithms,which are then investigated on diﬀerent DVRPtest cases.The results show that the proposed ACO algorithms achievepromising results,especially when elitism-based immigrants are used.1IntroductionIn the vehicle routing problem(VRP),a number of vehicles with limited capacity are routed in order to satisfy the demand of all customers at a minimum cost (usually the total travel time).Ant colony optimization(ACO)algorithms have shown good performance for the VRP,where a population of ants cooperate and construct vehicle routes[5].The cooperation mechanism of ants is achieved via their pheromone trails,where each ant deposits pheromone to its trails and the remaining ants can exploit it[2].The dynamic VRP(DVRP)is closer to a real-world application since the traﬃc jams in the road system are considered.As a result,the travel time be-tween customers may change depending on the time of the day.In dynamic optimization problems(DOPs)the moving optimum needs to be tracked over time.ACO algorithms can adapt to dynamic changes since they are inspired from nature,which is a continuous adaptation process[9].In practice,they can adapt by transferring knowledge from past environments[1].The challenge of such algorithms is how quickly they can react to dynamic changes in order to maintain the high quality of output instead of premature convergence.C.Di Chio et al.(Eds.):EvoApplications2012,LNCS7248,pp.519–528,2012.c Springer-Verlag Berlin Heidelberg2012520M.Mavrovouniotis and S.YangDeveloping strategies for ACO algorithms to deal with premature conver-gence and address DOPs has attracted a lot of attention,which includes local and global restart strategies[7],memory-based approaches[6],pheromone ma-nipulation schemes to maintain diversity[4],and immigrants schemes to increase diversity[11,12].These approaches have been applied to the dynamic travelling salesman problem(DTSP),which is the simplest case of a DVRP,i.e.,only one vehicle is used.The ACO algorithms that are integrated with immigrants schemes have shown promising results on the DTSP where immigrant ants re-place the worst ants in the population every iteration[11].In this paper,we integrate two immigrants schemes,i.e.,random immigrants and elitism-based immigrants,to ACO algorithms and apply them to the DVRP with traﬃc factor.The aim of random immigrants ACO(RIACO)is to increase the diversity in order to adapt well in DOPs,and the aim of elitism-based im-migrants ACO(EIACO)is to generate guided diversity to avoid randomization.The rest of the paper is organized as follows.Section2describes the problem we try to solve,i.e.,the DVRP with traﬃc factors.Section3describes the ant colony system(ACS),which is one of the best performing algorithms for the VRP.Section4describes our proposed approaches where we incorporate immigrants schemes with ACO.Section5describes the experiments carried out by comparing RIACO and EIACO with ACS.Finally,Section6concludes this paper with directions for future work.2The DVRP with Traﬃc JamsThe VRP has become one of the most popular combinatorial optimization prob-lems,due to its similarities with many real-world applications.The VRP is classiﬁed as NP-hard[10].The basic VRP can be described as follows:a number of vehicles with aﬁxed capacity need to satisfy the demand of all the customers, starting from and returning to the depot.Usually,the VRP is represented by a complete weighted graph G=(V,E), with n+1nodes,where V={u0,...,u n}is a set of vertices corresponding to the customers(or delivery points)u i(i=1,···,n)and the depot u0and E={(u i,u j):i=j}is a set of edges.Each edge(u i,u j)is associated with a non-negative d ij which represents the distance(or travel time)between u i and u j.For each customer u i,a non-negative demand D i is given.For the depot u0, a zero demand is associated,i.e.,D0=0.The aim of the VRP is toﬁnd the route(or a set of routes)with the lowest cost without violating the following constraints:(1)every customer is visited exactly once by only one vehicle;(2)every vehicle starts andﬁnishes at the depot;and (3)the total demand of every vehicle route must not exceed the vehicle capacity Q.The number of routes identiﬁes the corresponding number of vehicles used to generate one VRP solution,which is notﬁxed but chosen by the algorithm.The VRP becomes more challenging if it is subject to a dynamic environment. There are many variations of the DVRP,such as the DVRP with dynamic de-mand[14].In this paper,we generate a DVRP with traﬃc factors,where eachAnt Colony Optimization with Immigrants Schemes for the DVRP521 edge(u i,u j)is associated with a traﬃc factor t ij.Therefore,the cost to travel from u i to u j is c ij=d ij×t ij.Furthermore,the cost to travel from u j to u i may diﬀer due to diﬀerent traﬃc factor.For example,one road may have more traﬃc in one direction and less traﬃc in the opposite direction.Every f iterations a random number R∈[F L,F U]is generated to represent potential traﬃc jams,where F L and F U are the lower and upper bounds of the traﬃc factor,respectively.Each edge has a probability m to have a traﬃc factor, by generating a diﬀerent R to represent high and low traﬃc jams on diﬀerent roads,i.e.,t ij=1+R,where the traﬃc factor of the remaining edges is set to1 (indicates no traﬃc).Note that f and m represent the frequency and magnitude of changes in the DVRP,respectively.3ACO for the DVRPThe ACO metaheuristic consists of a population ofμants where they construct solutions and share their information with the others via their pheromone trails. Theﬁrst ACO algorithm developed is the Ant System(AS)[2].Many variations of the AS have been developed over the years and applied to diﬃcult optimization problems[3].The best performing ACO algorithm for the DVRP is the ACS[13].There is a multi-colony variation of this algorithm applied to the VRP with time win-dows[5].However,in this paper we consider the single colony which has been applied to the DVRP[13].Initially,all the ants are placed on the depot and all pheromone trails are initialized with an equal amount.With a probability1−q0, where0≤q0≤1is a parameter of the pseudo-random proportional decision rule(usually0.9for ACS),an ant k chooses the next customer j from customeri,as follows:p k ij=⎧⎨⎩[τij]α[ηij]βl∈N k i[τil]α[ηil]β,if j∈N k i,0,otherwise,(1)whereτij is the existing pheromone trail between customers i and j,ηij is the heuristic information available a priori,which is deﬁned as1/c ij,where c ij isthe distance travelled(as calculated in Section2)between customers i and j, N k i denotes the neighbourhood of unvisited customers of ant k when its current customer is i,andαandβare the two parameters that determine the relativeinﬂuence of pheromone trail and heuristic information,respectively.With the probability q0,the ant k chooses the next customer with the maximum proba-bility,i.e.,[τ]α[η]β,and not probabilistically as in Eq.(1).However,if the choice of the next customer leads to an infeasible solution,i.e.,exceed the maximum capacity Q of the vehicle,the depot is chosen and a new vehicle route starts.When all ants construct their solutions,the best ant retraces the solution and deposits pheromone globally according to its solution quality on the correspond-ing trails,as follows:τij←(1−ρ)τij+ρΔτbestij,∀(i,j)∈Tbest,(2)522M.Mavrovouniotis and S.Yangwhere0<ρ≤1is the pheromone evaporation rate andΔτbestij =1/C best,whereC best is the total cost of the T best tour.Moreover,a local pheromone update is performed every time an ant chooses another customer j from customer i as follows:τij←(1−ρ)τij+ρτ0,(3) whereρis deﬁned as in Eq.(2)andτ0is the initial pheromone value.The pheromone evaporation is the mechanism that eliminates the areas with high intensity of pheromones that are generate by ants,due to stagnation be-haviour1,in order to adapt well to the new environment.The recovery time depends on the size of the problem and magnitude of change.4ACO with Immigrants Schemes for the DVRP4.1FrameworkThe framework of the proposed algorithms is based on the ACO algorithms that were used for the DTSP[11,12].It will be interesting to observe if the framework based on immigrants schemes is beneﬁcial for more realistic problems,such as the DVRP with traﬃc factors,as described in Section2.The initial phase of the algorithm and the solution construction of the ants are the same with the ACS;see Eq.(1).The diﬀerence of the proposed framework is that it uses a short-term memory every iteration t,denoted as k short(t),of limited size,i.e.,K s,which is associated with the pheromone matrix.Initially, k short(0)is empty where at the end of the iteration the K s best ants will be added to k short(t).Each ant k that enters k short(t)deposits a constant amount of pheromone to the corresponding trails,as follows:τij←τij+Δτk ij,∀(i,j)∈T k,(4)whereΔτk ij=(τmax−τ0)/K s and T k is the tour of ant k.Here,τmax andτ0are the maximum and initial pheromone value,respectively.Every iteration the ants from k short(t−1)are replaced with the K s best ants from iteration t,a negative update is performed to their pheromone trails,as follows:τij←τij−Δτk ij,∀(i,j)∈T k,(5) whereΔτij and T k are deﬁned as in Eq.(4).This is because no ants can survive in more than one iteration because of the dynamic environment.In addition,immigrant ants replace the worst ants in k short(t)every iteration and further adjustments are performed to the pheromone trails since k short(t) changes.The main concern when dealing with immigrants schemes is how to generate immigrant ants,that represent feasible solutions.1A term used when all ants follow the same path and construct the same solution.Ant Colony Optimization with Immigrants Schemes for the DVRP523 4.2Random Immigrants ACO(RIACO)Traditionally,the immigrants are randomly generated and replace other ants in the population to increase the diversity.A random immigrant ant for the DVRP is generated as follows.First,the depot is added as the starting point; then,an unvisited customer is randomly selected as the next point.This process is repeated until theﬁrst segment(starting from the most recent visit to the depot)of customers do not violate the capacity constraint.When the capacity constraint is violated the depot is added and another segment of customers starts.When all customers are visited the solution will represent one feasible VRP solution.Considering the proposed framework described above,before the pheromone trails are updated,a set S ri of r×K s immigrants are generated to replace the worst ants in k short(t),where r is the replacement rate.RIACO has been found to perform better in fast and signiﬁcantly changing environments for the DTSP[11].This is because when the changing environ-ments are not similar it is better to randomly increase the diversity instead of knowledge transfer.Moreover,when the environmental changes are fast the time is not enough to gain useful knowledge in order to transfer it.However,there is a high risk of randomization with RIACO that may disturb the optimization process.A similar behaviour is expected for the DVRP.4.3Elitism-Based Immigrants ACO(EIACO)Diﬀerently from RIACO,which generates diversity randomly with the immi-grants,EIACO generates guided diversity by the knowledge transferred from the best ant of the previous environment.An elitism-based immigrant ant for the DVRP is generated as follows.The best ant of the previous environment is selected in order to use it as the base to generate elitism-based immigrants.The depots of the best ant are removed and adaptive inversion is performed based on the inver-over operator[8].When the inversion operatorﬁnishes,the depots are added so that the capacity constraint is satisﬁed in order to represent one feasible VRP solution.Considering the proposed framework above,on iteration t,the elite ant from k short(t−1)is used as the base to generate a set S ei of r×K s immigrants,where r is the replacement rate.The elitism-based immigrants replace the worst ants in k short(t)before the pheromone trails are updated.The EIACO has been found to perform better in slowly and slightly changing environments for the DTSP[11].This is because the knowledge transferred when the changing environments are similar will be more useful.However,there is a risk to transfer too much knowledge and start the optimization process from a local optimum and get stuck there.A similar behaviour is expected for the DVRP.524M.Mavrovouniotis and S.Yang5Simulation Experiments5.1Experimental SetupIn the experiments,we compare the proposed RIACO and EIACO with the existing ACS,described in Section3.All the algorithms have been applied to the vrp45,vrp72,and vrp135problem instances2.To achieve a good balance between exploration and exploitation,most of the parameters have been obtained from our preliminary experiments where others have been inspired from literature[11].For all algorithms,μ=50ants are used,α=1,β=5,andτ0=1/n.For ACS,q0=0.9,andρ=0.7.Note that a lower evaporation rate has been used for ACS,i.e.ρ=0.1,with similar or worseresults.For the proposed algorithms,q0=0.0,K s=10,τmax=1.0and r=0.4.For each algorithm on a DVRP instance,N=30independent runs were executed on the same environmental changes.The algorithms were executed for G=1000iterations and the overall oﬄine performance is calculated as follows:P offline=1GGi=1⎛⎝1NNj=1P∗ij⎞⎠(6)where P∗ij deﬁnes the tour cost of the best ant since the last dynamic change of iteration i of run j[9].The value of f was set to10and100,which indicate fast and slowly changing environments,respectively.The value of m was set to0.1,0.25,0.5,and0.75, which indicate the degree of environmental changes from small,to medium,to large,respectively.The bounds of the traﬃc factor are set as F L=0and F U=5. As a result,eight dynamic environments,i.e.,2values of f×4values of m, were generated from each stationary VRP instance,as described in Section2,to systematically analyze the adaptation and searching capability of each algorithm on the DVRP.5.2Experimental Results and AnalysisThe experimental results regarding the oﬄine performance of the algorithms are presented in Table1and the corresponding statistical results of Wilcoxon rank-sum test,at the0.05level of signiﬁcance are presented in Table2.Moreover,to better understand the dynamic behaviour of the algorithms,the results of the largest problem instance,i.e.,vrp135,are plotted in Fig.1with f=10,m=0.1 and m=0.75,and f=100,m=0.1and m=0.75,for theﬁrst500iterations. From the experimental results,several observations can be made by comparing the behaviour of the algorithms.First,RIACO outperforms ACS in all the dynamic test cases;see the results of RIACO⇔ACS in Table2.This validates our expectation that ACS need 2Taken from the Fisher benchmark instances available athttp://neo.lcc.uma.es/radi-aeb/WebVRP/Ant Colony Optimization with Immigrants Schemes for the DVRP525 parison of algorithms regarding the results of the oﬄine performancef=10f=100m⇒0.10.250.50.750.10.250.50.75Alg.&Inst.vrp45ACS897.5972.51205.61648.0883.4929.11120.21536.9RIACO841.2902.41089.51482.9834.9867.51016.11375.1EIACO840.1899.81083.81473.5839.8860.61009.11355.5Alg.&Inst.vrp72ACS305.3338.6426.2596.2297.3324.6412.7547.9RIACO294.4322.8401.7562.5280.6303.5375.2489.6EIACO289.9319.4397.8557.0276.2298.5366.7476.5Alg.&Inst.vrp135ACS1427.71567.31967.42745.71383.71519.41820.52536.2RIACO1417.81554.21922.12676.01353.11457.21698.62358.4EIACO1401.31542.11907.62663.11329.11444.31668.52293.8Table2.Statistical tests of comparing algorithms regarding the oﬄine performance, where“+”or“−”means that theﬁrst algorithm is signiﬁcantly better or the second algorithm is signiﬁcantly betterAlg.&Inst.vrp45vrp72vrp135f=10,m⇒0.10.250.50.750.10.250.50.750.10.250.50.75RIACO⇔ACS++++++++++++EIACO⇔ACS++++++++++++EIACO⇔RIACO++++++++++++f=100,m⇒0.10.250.50.750.10.250.50.750.10.250.50.75RIACO⇔ACS++++++++++++EIACO⇔ACS++++++++++++EIACO⇔RIACO−+++++++++++suﬃcient time to recover when a dynamic change occurs,which can be also observed from Fig.1in the environmental case with f=100.This is because the pheromone evaporation is the only mechanism used to eliminate pheromone trails that are not useful to the new environment,and may bias the population to areas that are not near the new optimum.On the other hand,RIACO uses the proposed framework where the pheromone trails exist only in one iteration.Second,EIACO outperforms ACS in all the dynamic test cases as the RI-ACO;see the results EIACO⇔ACS in Table2.This is due to the same reasons RIACO outperforms the traditional ACS.However,EIACO outperforms RI-ACO in almost all dynamic test cases;see the results of EIACO⇔RIACO in Table2.In slowly and slightly changing environments EIACO has suﬃcient time to gain knowledge from the previous environment,and the knowledge transferred has more chances to help when the changing environments are similar.However, on the smallest problem instance,i.e.,vrp45,with f=100and m=0.1RIACO performs better than EIACO.This validates our expectation where too much526M.Mavrovouniotis and S.Yang1300 1350 1400 1450 1500 1550 16000100200300400500O f f l i n e P e r f o r m a n c eIterationvrp135 - f = 10, m = 0.1ACS RIACO EIACO 2200 2400 2600 2800 3000 3200 34000100200300400500O f f l i n e P e r f o r m a n c eIteration vrp135 - f = 10, m = 0.75ACS RIACO EIACO 1200 1250 130013501400 1450 1500 1550 16000100200300400500O f f l i n e P e r f o r m a n c e Iteration vrp135 - f = 100, m = 0.1ACS RIACO EIACO 2200 2400 2600 2800 3000 3200 34000100200300400500O f f l i n e P e r f o r m a n c eIterationvrp135 - f = 100, m = 0.75ACS RIACO EIACOFig.1.Oﬄine performance of algorithms for diﬀerent dynamic test problems 1300 1350 1400 1450 15000.00.20.40.60.8 1.0O f f l i n e P e r f o r m a n c e r vrp135, f = 100, m = 0.1RIACO EIACO ACS 2200 2300 2400 2500 2600 27000.00.20.40.60.8 1.0O f f l i n e P e r f o r m a n c ervrp135, f = 100, m = 0.75RIACO EIACO ACS Fig.2.Oﬄine performance of RIACO and EIACO with diﬀerent replacement rates against the performance of ACS in slowly changing environmentsknowledge transferred does not always mean better results in dynamic environ-ments.On the other hand RIACO,was expected to perform better than EIACO in fast and signiﬁcantly changing environments,since the random immigrants only increase the diversity,but that it is not the case.This may be possibly because of too much randomization that may disturb the optimization process and requires further investigation regarding the eﬀect of the immigrant ants.Ant Colony Optimization with Immigrants Schemes for the DVRP527 Third,in order to investigate the eﬀectiveness of the immigrants schemes,fur-ther experiments have been performed on the same problem instances with the same parameters used before but with diﬀerent immigrant replacement rates, i.e.,r∈{0.0,0.2,0.4,0.6,0.8,1.0}.In Fig.2the oﬄine performance of RIACO and EIACO with the varying replacement rates are presented3,against the ACS performance,where r=0.0means that no immigrants are generated to re-place ants in the k short(t).The results conﬁrm our expectation above,where the random immigrants in RIACO sometimes may disturb the optimization and de-grade the performance.On the other hand,elitism-based immigrants in EIACO improve the performance,especially in slightly changing environments.Finally,the proposed framework performs better than ACS,even if no immi-grants are generated;see Fig.2.The RIACO with r=1.0performs worse than the ACS,whereas the EIACO with r=1.0better than ACS.This is because RIACO destroys all the knowledge transferred to the k short(t)from the ants of the previous iteration with random immigrants,whereas EIACO destroys that knowledge but transfers new knowledge using the best ant from the previous iteration.6ConclusionsDiﬀerent immigrants schemes have been successfully applied to evolutionary al-gorithms and ACO algorithms to address diﬀerent DOPs[11,16].ACO-based algorithms with immigrants,i.e.,RIACO and EIACO,have shown good perfor-mance on diﬀerent variations of the DTSP[11,12].In this paper,we modify and apply such algorithms to address the DVRP with traﬃc factors,which is closer to a real-world application.The immigrant ants are generated either randomly or using the previous best ant as the base and replace the worst ones in the pop-ulation.The aim is to maintain the diversity of solutions and transfer knowledge from previous environments in order to adapt well in DOPs.Comparing RIACO and EIACO with ACS,one of the best performing ACO al-gorithms for VRP,on diﬀerent test cases of DVRPs,the following concluding re-marks can be drawn.First,the proposed framework used to integrate ACO with immigrants schemes,performs better than the traditional framework,even when immigrant ants are not generated.Second,EIACO is signiﬁcantly better than RI-ACO and ACS in almost all dynamic test cases.Third,RIACO is signiﬁcantly bet-ter than ACS in all dynamic test cases.Finally,the random immigrants may disturb the optimization process with a result to degrade the performance,whereas elitism-based immigrants transfers knowledge with a result to improves the performance for the DVRP with traﬃc factor.An obvious direction for future work is to hybridize the two immigrants schemes.However,from our preliminary results the performance of the hybrid scheme is better than RIACO but worse than EIACO in all dynamic test cases. Therefore,toﬁnd another way to achieve a good balance between the knowledge 3The experimental results of the remaining problem instances and dynamic test cases are similar for EIACO,whereas for RIACO there is an improvement when r>0.0 on the smallest problem instance.528M.Mavrovouniotis and S.Yangtransferred and the diversity generated would be interesting for future work.An-other future work is to integrate memory-based immigrants with ACO,which have also performed well on the DTSP[12],to the DVRP with traﬃc factors. References1.Bonabeau,E.,Dorigo,M.,Theraulaz,G.:Swarm Intelligence:From Natural toArtiﬁcial Systems.Oxford University Press,New York(1999)2.Dorigo,M.,Maniezzo,V.,Colorni,A.:Ant system:optimization by a colony ofcooperating agents.IEEE Trans.on Syst.Man and Cybern.Part B:Cybern.26(1), 29–41(1996)3.Dorigo,M.,St¨u tzle,T.:Ant Colony Optimization.The MIT Press,London(2004)4.Eyckelhof,C.J.,Snoek,M.:Ant Systems for a Dynamic TSP.In:ANTS2002:Proc.of the3rd Int.Workshop on Ant Algorithms,pp.88–99(2002)5.Gambardella,L.M.,Taillard, E.,Agazzi,G.:MACS-VRPTW:A multiple antcolony system for vehicle routing problems with time windows.In:Corne,D.,et al.(eds.)New Ideas in Optimization,pp.63–76(1999)6.Guntsch,M.,Middendorf,M.:Applying Population Based ACO to Dynamic Op-timization Problems.In:Dorigo,M.,Di Caro,G.A.,Sampels,M.(eds.)Ant Algo-rithms2002.LNCS,vol.2463,pp.111–122.Springer,Heidelberg(2002)7.Guntsch,M.,Middendorf,M.:Pheromone Modiﬁcation Strategies for Ant Algo-rithms Applied to Dynamic TSP.In:Boers,E.J.W.,Gottlieb,J.,Lanzi,P.L.,Smith, R.E.,Cagnoni,S.,Hart,E.,Raidl,G.R.,Tijink,H.(eds.)EvoIASP2001,EvoWork-shops2001,EvoFlight2001,EvoSTIM2001,EvoCOP2001,and EvoLearn2001.LNCS,vol.2037,pp.213–222.Springer,Heidelberg(2001)8.Tao,G.,Michalewicz,Z.:Inver-over Operator for the TSP.In:Eiben, A.E.,B¨a ck,T.,Schoenauer,M.,Schwefel,H.-P.(eds.)PPSN1998.LNCS,vol.1498, pp.803–812.Springer,Heidelberg(1998)9.Jin,Y.,Branke,J.:Evolutionary optimization in uncertain environments-a survey.IEEE Trans.on put.9(3),303–317(2005)bbe,M.,Laporte,G.,Mercure,H.:Capacitated vehicle routing on trees.Oper-ations Research39(4),616–622(1991)11.Mavrovouniotis,M.,Yang,S.:Ant Colony Optimization with Immigrants Schemesin Dynamic Environments.In:Schaefer,R.,Cotta,C.,Ko l odziej,J.,Rudolph,G.(eds.)PPSN XI.LNCS,vol.6239,pp.371–380.Springer,Heidelberg(2010)12.Mavrovouniotis,M.,Yang,S.:Memory-Based Immigrants for Ant Colony Opti-mization in Changing Environments.In:Di Chio,C.,Cagnoni,S.,Cotta,C.,Ebner, M.,Ek´a rt,A.,Esparcia-Alc´a zar,A.I.,Merelo,J.J.,Neri,F.,Preuss,M.,Richter,H.,Togelius,J.,Yannakakis,G.N.(eds.)EvoApplications2011,Part I.LNCS,vol.6624,pp.324–333.Springer,Heidelberg(2011)13.Montemanni,R.,Gambardella,L.,Rizzoli,A.,Donati,A.:Ant colony system fora dynamic vehicle routing problem.Journal of Combinatorial Optimization10(4),327–343(2005)14.Psaraftis,H.:Dynamic vehicle routing:status and prospects.Annals of OperationsResearch61,143–164(1995)15.Rizzoli,A.E.,Montemanni,R.,Lucibello,E.,Gambardella,L.M.:Ant colony op-timization for real-world vehicle routing problems-from theory to applications.Swarm Intelli.1(2),135–151(2007)16.Yang,S.:Genetic algorithms with memory and elitism based immigrants in dy-namic put.16(3),385–416(2008)。

OrganizationalGoals

Organizational GoalsOrganizational goals help us focus and filter our learning on the system’s highest priorities. In this way, professional development and organizational improvement are integrally related. The growth of professionals can contribute to the organization as a whole, but individual growth without the organizational context is insufficient and inefficient in helping the organization to achieve its strategic goals. In the absence of organizational direction (vision and goals), the impact of professional development becomes a matter of luck rather than the result of a deliberate allocation of resources.When we view this relationship from the opposite perspec-tive, we see an equally strong case for linking the two. Imagine a district or school strategy for improvement that ignores the need to develop the people who will be responsible for implementing the improvements. It is hard to divorce the two, to imagine one without the other—especially in an educational environment where the organization is almost synonymous with its people.The Kimberly Area School District in Kimberly, Wisconsin, provides a dramatic example of how a district-wide vision, accom-panied by a clear set of SMART goals, not only shaped profes-sional development practices, but ultimately redefined the role of professional development as the key strategic process for improv-ing student results. In Kimberly, every decision is based on the pursuit of goals; everything they do to achieve their goals is con-sidered professional development.In the mid 1990s, the board, central administration, and prin-cipals published a strategic plan for the district that they called “Mission Possible: Raise Student Achievement.” The plan includ-ed a goal that by the 2002–2003 school year at least90% of stu-dents would be proficient or advanced readers as assessed by the Wisconsin Reading Comprehension T est (WRCT). The leaders believed strongly that if children could read and read well, the scores on other measures of achievement in other areas of learn-ing would also dramatically improve.Solution Tree121At the time that the plan was created, the average proficien-cy rate for young readers in Kimberly was at 61%—below the state average—on the WRCT and between 40% and 50% on the Wisconsin Knowledge and Concepts Exams (WKCE). The dis-trict’s goal of 90% seemed unattainable and unrealistic to those deep inside the organization. In fact, there were accusations that it would “hurt kids”; many felt that becoming so narrowly focused on reading meant that the social, emotional, artistic, physical, and broader knowledge-based needs of students would be ignored. Furthermore, it was feared that such dramatic gains could only be accomplished if teachers did nothing but teach to the test.Indeed, this goal would be impossible if the district had not supported a fundamental change in how teachers taught and how students learned. In particular, the plan for achieving this lofty goal needed to include a change in how professional development was delivered, how classrooms were resourced, what content was taught, and ultimately, what got removed from the already over-filled plates of the staff.The graph in figure 5.1 illustrates Kimberly’s 7-year journey. Based on the reading performance of third graders as measured by the WCRT, Mission Possible was indeed possible! Kimberly was the only district to improve its rank on all three subtests in every year at every grade level tested. As a result, based on a for-mula that included a variety of criteria, Kimberly ranked first among all Wisconsin districts as the most “improving” school dis-trict. It moved from the bottom half of the districts in 1998–1999 to the top 20% in the state in 2003–2004.Recall that one of the fears of staff was that if the district were to take such a narrow approach in targeting resources and staff development on literacy, other areas of learning would be com-promised. In direct opposition, the district’s leadership believed that by focusing on reading, other areas would benefit. A look at a recent study conducted by the Wisconsin T axpayer’s Alliance (2004) shows how Kimberly fared across the board (figure 5.2, page 124).122Solution Tree 123Fi g u r e 5.1: K i m b e r l y , W i s c o n s i n , R e a d i n g C o m p r e h e n s i o n T e s t —7-Y e a r C o m p a r i s o n .P e r c e n t p r o f i c i e n t a n d a d v a n c e d.124Figure 5.3:Kimberly, Wisconsin, WKCE 7-Year Comparisonsfor Fourth, Eighth, and Tenth Grades.Solution Tree 125The development model at the elementary level includes ongoing collegial learning teams where teachers regularly work and learn together, sharing their strategies and examining their data. One of the formal graduate courses embedded in this model begins in August with a 2-day intensive training session on designing and implementing standards-based lessons. The out-come of this session is a lesson that is implemented within the first week of school. Following the implementation, a teacher has time to reflect on student learning from the lesson and share insights with the administrative supervisor, who has been trained as a stan-dards-based learning coach.In phase two, a new standards-based lesson is developed and then implemented while a trained coach observes. The two reflect together and discuss teacher learning and improvements. This cycle is repeated throughout the year.Another approach creates a learning laboratory where chil-dren participate in a summer session designed to give teachers a safe environment in which to receive valuable feedback on imple-mentation strategies. In these sessions, the teacher-student ratio is two to one—two teachers for every one child. One teacher teaches while another teacher observes. Then, in a fashion simi-lar to Japanese lesson study (Lewis & T suchida, 1998; Watanabe, 2002), they come together to dialogue about what worked, what did not work, and what could be done differently to achieve max-imum results. Then the teachers rotate roles so that both have the opportunity to teach, to observe, and to give and receive descrip-tive feedback.At the middle school, every teacher participates as a member of a “tuning protocol” team, “a [professional development] pro-cess through which educators can hone their skills by examin-ing student work in a supportive, problem-solving group”(Easton, 2002, p. 28). In Kimberly, the process was designed by and is led by teacher leaders. It is cross-disciplinary, happens dur-ing the day, and is based on researched best practices in assess-ment for learning.126The Importance of LeadershipIn a system, it is not enough to change the behaviors of one part of the organization—in this case, the teachers. It is the very nature of systems that all of the individuals and their respective roles are symbiotic and as such must realign in support of the change effort. Thus, Kimberly also needed to change how lead-ership occurred so that there were systematic and informed sup-port mechanisms in place for teachers to successfully implement what they were learning. “It’s all about leadership,” says Bowen-Eggebraaten. “Leadership is no longer a ‘position’ assigned to a certain group of individuals; it is the responsibility of everyone in the organization. All individuals are expected to take respon-sibility for improving student learning results. When a system is accountable for results, it translates into shared leadership.”This is precisely what Lambert (1998) refers to as leadership capacity, “skillful, broad-based involvement in the work of leader-ship” (p. 3).Solution Tree127。

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Ant Colony System:A Cooperative Learning Approach to the Traveling Salesman Problem Marco Dorigo,Senior Member,IEEE,and Luca Maria Gambardella,Member,IEEEAbstract—This paper introduces the ant colony system(ACS), a distributed algorithm that is applied to the traveling salesman problem(TSP).In the ACS,a set of cooperating agents called ants cooperate toﬁnd good solutions to TSP’s.Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation.The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evo-lutionary computation,and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSP’s.Index Terms—Adaptive behavior,ant colony,emergent behav-ior,traveling salesman problem.I.I NTRODUCTIONT HE natural metaphor on which ant algorithms are based is that of ant colonies.Real ants are capable ofﬁnding the shortest path from a food source to their nest[3],[22]without using visual cues[24]by exploiting pheromone information. While walking,ants deposit pheromone on the ground and follow,in probability,pheromone previously deposited by other ants.In Fig.1,we show a way ants exploit pheromone toﬁnd a shortest path between two points.Consider Fig.1(a):ants arrive at a decision point in which they have to decide whether to turn left or right.Since they have no clue about which is the best choice,they choose randomly.It can be expected that,on average,half of the ants decide to turn left and the other half to turn right.This happens both to ants moving from left to right(those whose name begins with an L)and to those moving from right to left (name begins with an R).Fig.1(b)and(c)shows what happens in the immediately following instants,supposing that all ants walk at approximately the same speed.The number of dashed lines is roughly proportional to the amount of pheromone that the ants have deposited on the ground.Since the lower path is shorter than the upper one,more ants will visit it on average, and therefore pheromone accumulates faster.After a short transitory period the difference in the amount of pheromone on the two paths is sufﬁciently large so as to inﬂuence the decision of new ants coming into the system[this is shown byManuscript received October7,1996;revised January18,1997and February3,1997.This work was supported by the Swiss National Science Fund Contract21-45653.95.M.Dorigo is with IRIDIA,Universit`e Libre de Bruxelles,1050Bruxelles, Belgium(e-mail:mdorigo@ulb.ac.be).L.M.Gambardella is with IDSIA,6900Lugano,Switzerland. Publisher Item Identiﬁer S1089-778X(97)03303-1.Fig.1(d)].From now on,new ants will prefer in probability to choose the lower path,since at the decision point they perceive a greater amount of pheromone on the lower path.This in turn increases,with a positive feedback effect,the number of ants choosing the lower,and shorter,path.Very soon all ants will be using the shorter path.The above behavior of real ants has inspired ant system, an algorithm in which a set of artiﬁcial ants cooperate to the solution of a problem by exchanging information via pheromone deposited on graph edges.The ant system has been applied to combinatorial optimization problems such as the traveling salesman problem(TSP)[7],[8],[10],[12]and the quadratic assignment problem[32],[42].The ant colony system(ACS),the algorithm presentedin this article,builds on the previous ant system in thedirection of improving efﬁciency when applied to symmetricand asymmetric TSP’s.The main idea is that of having a setof agents,called ants,search in parallel for good solutions tothe TSP and cooperate through pheromone-mediated indirectand global rmally,each ant constructsa TSP solution in an iterative way:it adds new cities to apartial solution by exploiting both information gained frompast experience and a greedy heuristic.Memory takes the formof pheromone deposited by ants on TSP edges,while heuristicinformation is simply given by the edge’s length.The main novel idea introduced by ant algorithms,whichwill be discussed in the remainder of the paper,is the syner-gistic use of cooperation among many relatively simple agentswhich communicate by distributed memory implemented aspheromone deposited on edges of a graph.This paper is organized as follows.Section II puts theACS in context by describing ant system,the progenitorof the ACS.Section III introduces the ACS.Section IV isdedicated to the study of some characteristics of the ACS:We study how pheromone changes at run time,estimate theoptimal number of ants to be used,observe the effects ofpheromone-mediated cooperation,and evaluate the role thatpheromone and the greedy heuristic have in ACS performance.Section V provides an overview of results on a set of standardtest problems and comparisons of the ACS with well-knowngeneral purpose algorithms like evolutionary computation andsimulated annealing.In Section VI we add local optimizationto the ACS,obtaining a new algorithm called ACS-3-opt.Thisalgorithm is compared favorably with the winner of the FirstInternational Contest on Evolutionary Optimization[5]onasymmetric TSP(ATSP)problems(see Fig.2),while it yieldsa slightly worse performance on TSP problems.Section VII is 1089–778X/97$10.00©1997IEEE(a)(b)(c)(d)Fig.1.How real ants ﬁnd a shortest path.(a)Ants arrive at a decision point.(b)Some ants choose the upper path and some the lower path.The choice is random.(c)Since ants move at approximately a constant speed,the ants which choose the lower,shorter,path reach the opposite decision point faster than those which choose the upper,longer,path.(d)Pheromone accumulates at a higher rate on the shorter path.The number of dashed lines is approximately proportional to the amount of pheromone deposited byants.Fig.2.The traveling salesman problem.dedicated to the discussion of the main characteristics of the ACS and indicates directions for further research.II.B ACKGROUNDAnt system [10]is the progenitor of all our research efforts with ant algorithms and was ﬁrst applied to the TSP,which is deﬁned in Fig.2.Ant system utilizes a graph representation which is the same as that deﬁned in Fig.2,augmented as follows:in addition to the costmeasure,called pheromone ,which is updated at runtime by artiﬁcial ants (ants for short).When ant system isapplied to symmetric instances of theTSP,,but when it is applied to asymmetric instances it is possiblethat .Informally,ant system works as follows.Each ant gener-ates a complete tour by choosing the cities according to aprobabilistic state transition rule ;ants prefer to move to cities which are connected by short edges with a high amount of pheromone.Once all ants have completed their tours a global pheromone updating rule (global updating rule,for short)is applied;a fraction of the pheromone evaporates on all edges (edges that are not refreshed become less desirable),and then each ant deposits an amount of pheromone on edges which belong to its tour in proportion to how short its tour was (in other words,edges which belong to many short tours are the edges which receive the greater amount of pheromone).The process is then iterated.The state transition rule used by ant system,called a random-proportional rule ,is given by (1),which gives theDORIGO AND GAMBARDELLA:ANT COLONY SYSTEM55Fig.3.The ACS algorithm.probability with whichant chooses to move to thecityis thepheromone,,is the set of cities that remain to bevisited byant (to make the solution feasible),and.In this way we favor thechoice of edges which are shorter and which have a greater amount of pheromone.In ant system,the global updating rule is implemented as follows.Once all ants have built their tours,pheromone is updated on all edges accordingto(2)wheretour done byantotherwise,andants are initiallypositionedonchooses thecitybiasedexploration56IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,VOL.1,NO.1,APRIL1997where,andhas to choose acitythen the bestedge,according to(3),is chosen(exploitation),otherwise anedge is chosen according to(1)(biased exploration).B.ACS Global Updating RuleIn ACS only the globally best ant(i.e.,the ant whichconstructed the shortest tour from the beginning of the trial)is allowed to deposit pheromone.This choice,together withthe use of the pseudo-random-proportionalrule,is intended tomake the search more directed:ants search in a neighborhoodof the best tour found up to the current iteration of thealgorithm.Global updating is performed after all ants havecompleted their tours.The pheromone level is updated byapplying the global updating rule of(4)global-best-touris thelength of the globally best tour from the beginning of the trial.As was the case in ant system,global updating is intendedto provide a greater amount of pheromone to shorter tours.Equation(4)dictates that only those edges belonging to theglobally best tour will receive reinforcement.We also testedanother type of global updating rule,called iteration-best,asopposed to the above called global-best,which instead used(the length of the best tour in the current iteration ofthe trial),in(4).Also,with iteration-best the edges whichreceive reinforcement are those belonging to the best tourof the current iteration.Experiments have shown that thedifference between the two schemes is minimal,with a slightpreference for global-best,which is therefore used in thefollowing experiments.C.ACS Local Updating RuleWhile building a solution(i.e.,a tour)of the TSP,ants visitedges and change their pheromone level by applying the localupdating rule of(5).Finally,wealso ran experiments in which local updating was not applied(i.e.,the local updating rule is not used,as was the case inant system).Results obtained running experiments(see Table I)on aset ofﬁve randomly generated50-city TSP’s[13],on theOliver30symmetric TSP[41]and the ry48p asymmetric TSP[35],essentially suggest that local updating is deﬁnitely usefuland that the local updating rule with yieldsworse performance than local updating withis the tour length produced by thenearest neighbor heuristic1[36]and.Thenumber of ants used isDORIGO AND GAMBARDELLA:ANT COLONY SYSTEM 57TABLE IA C OMPARISON OF L OCAL U PDATING R ULES .F IFTY -C ITY P ROBLEMS AND O LIVER 30W ERE S TOPPED A FTER 2500I TERATIONS ,W HILERY 48PW AS H ALTED A FTER 10000I TERATIONS.A VERAGES A RE O VER 25T RIALS .R ESULTS IN B OLD A RE THE B EST IN THE TABLEin Section IV-B).Regarding their initial positioning,ants are placed randomly,with at most one ant in each city.IV.A S TUDY OF S OME C HARACTERISTICS OFTHEACSA.Pheromone Behavior and Its Relation to Performance To try to understand which mechanism the ACS uses to direct the search we study how the pheromone-closeness prod-uct changes at run time.Fig.4shows how the pheromone-closeness product changes with the number of steps while ants are building a solution 2(steps refer to the inner loop in Fig.3:the abscissa goes therefore from 1tois the number of cities).Let us consider three families of edges (see Fig.4):i)those belonging to the last best tour (BE,best edges),ii)those which do not belong to the last best tour,but which did in one of the two preceding iterations (TE,testable edges),and iii)the remaining edges,that is,those that have never belonged to a best tour or have not in the last two iterations (UE,uninteresting edges).The average pheromone-closeness product is then computed as the average of pheromone-closeness values of all the edges within a family.The graph clearly shows that the ACS favors exploitation of edges in BE (BE edges are chosen withprobabilityants pointless).Experimental observation has shown that edges in BE,when ACS achieves a good performance,will be approximately downgraded to TE after an iteration of the algorithm (i.e.,one external loop in Fig.3;see also Fig.4)and that edges in TE will soon be downgraded to UE,unless they happen to belong to a new shortest tour.In Fig.5(a)and (b),we report two typical behaviors of pheromone level when the system has a good or a bad performance respectively.B.The Optimal Number of Ants Consider Fig.6.3Let58IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,VOL.1,NO.1,APRIL1997(a)(b)Fig.5.Families of edges classiﬁed according to different behavior with respect to the level of the pheromone-closeness product.Problem:Eil51[14].(a)Pheromone-closeness behavior when the system performance is good.Best solution found after 1000iterations:426, = =0:1.(b)Pheromone-closeness behavior when the system performance is bad.Best solution found after 1000iterations:465, = =0:9.rule is a ﬁrst-order linear recurrence relation of theform.Knowing that just beforeglobalupdatingof ants thatlocally update edges in BE is givenbyand ,which would tellhow,whichgives.Fig.7showsthat cooperation greatly improve s the probability of ﬁnding quickly an optimal solution.DORIGO AND GAMBARDELLA:ANT COLONY SYSTEM59Fig.8.Cooperating ants ﬁnd better solutions in a shorter time.Test problem:CCAO [21].Average on 25runs.The number of ants was set to m =4.parison between ACS standard,ACS with no heuristic (i.e.,we set =0),and ACS in which ants neither sense nor deposit pheromone.Problem:Oliver30.Averaged over 30trials,10000=m iterations per trial.In the second experiment (Fig.8)the best solution found is plotted as a function of time (ms)for cooperating and noncooperating ants.The number of ants is ﬁxed for bothcases:.It is interesting to note that in the cooperative case,after 300ms,the ACS always found the optimal solution,while noncooperating ants where not able to ﬁnd it after 800ms.During the ﬁrst 150ms (i.e.,before the two lines in Fig.8cross)noncooperating ants outperform cooperating ants:good values of pheromone level are still being learned,and therefore the overhead due to pheromone updating is not yet compensated by the advantages which pheromone can provide in terms of directing the search toward good solutions.D.The Importance of the Pheromone and the Heuristic FunctionExperimental results have shown that the heuristic function ACS performanceworsens signiﬁcantly (see the ACS no heuristic graph in Fig.9).Fig.9also shows the behavior of ACS in an experiment in which ants neither sense nor deposit pheromone (ACS no pheromone graph).The result is that not using pheromone also deteriorates performance.This is a further conﬁrmation of the results on the role of cooperation presented in Section IV-C.TABLE IIC OMPARISON OF ACS WITH O THER H EURISTICS ON R ANDOM I NSTANCES OF THE S YMMETRIC TSP.C OMPARISONS ON A VERAGET OUR L ENGTH O BTAINED ON F IVE 50-C ITY P ROBLEMSThe reason that ACS without the heuristic function performs better than ACS without pheromone is that in the ﬁrst case,although not helped by heuristic information,the ACS is still guided by reinforcement provided by the global updating rule in the form of pheromone,while in the second case the ACS reduces to a stochastic multigreedy algorithm.V.ACS:S OME C OMPUTATIONAL R ESULTSWe report on two sets of experiments.The ﬁrst set compares the ACS with other heuristics.The choice of the test problems was dictated by published results found in the literature.The second set tests the ACS on some larger problems.Here the comparison is performed only with respect to the optimal or the best known result.The behavior of the ACS is excellent in both cases.Most of the test problems can be found in TSPLIB:http://www.iwr.uni-heidelberg.de/iwr/comopt/soft/TSPLIB95/TSPLIB.html.When they are not available in this library we explicitly cite the reference where they can be found.Given that during an iteration of the algorithm,each ant produces a tour,the total number of tours generated in the reported results is given by the number of iterations multiplied by the number of ants.The result of each trial is given by the best tour generated by the ants.Each experiment consists of at least 15trials.parison with Other HeuristicsTo compare the ACS with other heuristics we consider two sets of TSP problems.The ﬁrst set comprises ﬁve randomly generated 50-city problems,while the second set is composed of three geometric problems 4of between 50and 100cities.It is important to test the ACS on both random and geometric instances of the TSP because these two classes of problems have structural differences that can make them difﬁcult for a particular algorithm and at the same time easy for another one.Table II reports the results on the random instances.The heuristics with which we compare the ACS are simulated annealing (SA),elastic net (EN),and self-organizing map (SOM).Results on SA,EN,and SOM are from [13]and [34].The ACS was run for 2500iterations using ten ants (this amounts to approximately the same number of tours searched4Geometricproblems are problems taken from the real world (for example,they are generated choosing real cities and real distances).60IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,VOL.1,NO.1,APRIL1997TABLE IIIC OMPARISON OF ACS WITH O THER H EURISTICS ON G EOMETRIC I NSTANCES OF THE S YMMETRIC TSP.W E R EPORT THE B EST I NTEGER T OUR L ENGTH,THE B ESTR EAL T OUR L ENGTH(IN P ARENTHESES),AND THE N UMBER OF T OURS R EQUIRED TO F IND THE B EST I NTEGER T OUR L ENGTH(IN S QUAREB RACKETS).N/A M EANS“N OT A V AILABLE.”I N THE L ASTC OLUMN THE O PTIMAL L ENGTH I S A V AILABLE O NLY FOR I NTEGER T OUR LENGTHSby the heuristics with which we compare our results).The ACS results are averaged over25trials.The best average tour length for each problem is in boldface:the ACS almost always offers the best performance.Table III reports the results on the geometric instances.The heuristics with which we compare the ACS in this case are a genetic algorithm(GA),evolutionary programming(EP),and simulated annealing(SA).The ACS is run for1250iterations using20ants(this amounts to approximately the same number of tours searched by the heuristics with which we compare our results).ACS results are averaged over15trials.In this case comparison is performed on the best results,as opposed to average results as in previous Table II(this choice was dictated by the availability of published results).The difference between integer and real tour length is that in theﬁrst case distances between cities are measured by integer numbers, while in the second case byﬂoating point approximations of real numbers.Results using EP are from[15],and those using GA are from [41]for Eil50and Eil75,and from[6]for KroA100.Results using SA are from[29].Eil50,Eil75are from[14]and are included in TSPLIB with an additional city as Eil51.tsp and Eil76.tsp.KroA100is also in TSPLIB.The best result for each problem is in boldface.Again,the ACS offers the best performance in nearly every case.Only for the Eil50problem does itﬁnd a slightly worse solution using real-valued distance as compared with EP,but the ACS only visits1830tours, while EP used100000such evaluations(although it is possible that EP found its best solution earlier in the run,this is not speciﬁed in the paper[15]).B.ACS on Some Bigger ProblemsWhen trying to solve big TSP problems it iscommonpractice[28],[35]to use a data structure known as a candidate list.A candidate list is a list of preferred cities to be visited; it is a static data structure which contains,for a given cityin our experiments. We implemented therefore a version of the ACS[20]which incorporates a candidate list:an ant in this extended version of the ACSﬁrst chooses the city to move to among those belonging to the candidate list.Only if none of the cities in the candidate list can be visited then it considers the rest of the cities.The ACS with the candidate list(see Table IV)was able toﬁnd good results for problems up to more than1500 cities.The time to generate a tour grows only slightly more than linearly with the number of cities(this is much better than the quadratic growth obtained without the candidate list); on a Sun Sparc-server(50MHz)it took approximately0.02 s of CPU time to generate a tour for the d198problem,0.05 s for the pcb442,0.07s for the att532,0.13s for the rat783, and0.48s for theﬂ1577(the reason for the more than linear increase in time is that the number of failures,that is,the number of times an ant has to choose the next city outside of the candidate list increases with the problem dimension).VI.ACS P LUS L OCAL S EARCHIn Section V we have shown that the ACS is competi-tive with other nature-inspired algorithms on some relatively simple problems.On the other hand,in past years much work has been done to deﬁne ad-hoc heuristics(see[25] for an overview)to solve the TSP.In general,these ad-hoc heuristics greatly outperform,on the speciﬁc problem of the TSP,general purpose algorithms approaches like evolutionary computation and simulated annealing.Heuristic approaches to the TSP can be classiﬁed as tour constructive heuristics and tour improvement heuristics(these last also called local opti-mization heuristics).Tour constructive heuristics(see[4]for an overview)usually start selecting a random city from the set of cities and then incrementally build a feasible TSP solution by adding new cities chosen according to some heuristic rule.For example,the nearest neighbor heuristic builds a tour by adding the closest node in terms of distance from the last node inserted in the path.On the other hand,tour improvement heuristics start from a given tour and attempt to reduce its length by exchanging edges chosen according to some heuristic rule until a local optimum is found(i.e.,until no further improvement is possible using the heuristic rule).The most used and well-known tour improvement heuristics are2-opt and3-opt[30], and Lin–Kernighan[31]in which,respectively,two,three, and a variable number of edges are exchanged.It has been experimentally shown[35]that,in general,tour improvementDORIGO AND GAMBARDELLA:ANT COLONY SYSTEM61TABLE IVACS P ERFORMANCE FOR S OME B IGGER G EOMETRIC P ROBLEMS(O VER15T RIALS).W E R EPORT THE I NTEGER L ENGTH OF THE S HORTEST T OUR F OUND,THEN UMBER OF T OURS R EQUIRED TO F IND I T,THE A VERAGE I NTEGER L ENGTH,THE S TANDARD D EVIATION,THE O PTIMAL S OLUTION(FOR FL1577W E G IVE,IN S QUAREB RACKETS,THE K NOWN L OWER AND U PPER B OUNDS,G IVEN THAT THE O PTIMAL S OLUTION I S N OT K NOWN),AND THE R ELATIVE E RROR OFACSheuristics produce better quality results than tour constructive heuristics.A general approach is to use tour constructive heuristics to generate a solution and then to apply a tour improvement heuristic to locally optimize it.It has been shown recently[25]that it is more effective to alternate an improvement heuristic with mutations of the last(or of the best)solution produced,rather than iteratively executing a tour improvement heuristic starting from solutions generated randomly or by a constructive heuristic.An example of successful application of the above alternate strategy is the work by Freisleben and Merz[17],[18],in which a genetic algorithm is used to generate new solutions to be locally optimized by atour improvement heuristic.The ACS is a tour construction heuristic which,likeFreisleben and Merz’s genetic algorithm,produces a set offeasible solutions after each iteration which are in some sensea mutation of the previous best solution.It is,therefore,areasonable guess that adding a tour improvement heuristic tothe ACS could make it competitive with the best algorithms.We therefore have added a tour improvement heuristic tothe ACS.To maintain the ACS’s ability to solve both TSP andATSP problems we have decided to base the local optimizationheuristic on a restricted3-opt procedure[25],[27]that,whileinserting/removing three edges on the path,considers only3-opt moves that do not revert the order in which the cities arevisited.In this case it is possible to change three edges onthe touris turned on when a search for an improving movestarting from is turned offagain when a move involving,the procedure also considers possible2-opt moves with62IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,VOL.1,NO.1,APRIL1997Fig.10.The ACS-3-opt algorithm.TABLE VR ESULTS O BTAINED BY ACS-3-O PT ON ATSP P ROBLEMS T AKEN FROM THE F IRST I NTERNATIONAL C ONTEST ON E VOLUTIONARY O PTIMIZATION[5].W E R EPORT THE L ENGTH OF THE B EST T OUR F OUND BY ACS-3-O PT,THE CPU T IME U SED TO F IND IT,THE A VERAGE L ENGTH OF THE B EST T OUR F OUND AND THE A VERAGE CPU T IME U SED TO F IND IT,THE O PTIMAL L ENGTH,AND THE R ELATIVE E RROR OF THE A VERAGE R ESULT WITH R ESPECT TO THE O PTIMAL S OLUTIONprocessor due to the sequential implementation of ACS-3-opt.For each test problem,ten trials have been executed.ACS-3-opt parameters were set to the following values(except ifdifferently indicated):,andandthat are not contained inTABLE VIC OMPARISON B ETWEEN ACS-3-O PT AND ATSP-GA ON ATSP P ROBLEMS T AKEN FROM THEF IRST I NTERNATIONAL C ONTEST ON E VOLUTIONARY O PTIMIZATION [5].W E R EPORT THE A VERAGE L ENGTH OF THE B EST T OUR F OUND ,THE A VERAGE CPU T IME U SED TO F IND IT ,AND THE R ELATIVE E RROR WITH R ESPECT TO THE O PTIMAL S OLUTION FOR B OTH APPROACHESTABLE VIIR ESULTS O BTAINED BY ACS-3-O PT ON TSP P ROBLEMS T AKEN FROM THE F IRST I NTERNATIONAL C ONTEST ON E VOLUTIONARY O PTIMIZATION [5].W E R EPORT THE L ENGTH OF THE B EST T OUR F OUND BY ACS-3-O PT ,THE CPU T IME U SED TO F IND I T ,THE A VERAGE L ENGTH OF THE B EST T OUR F OUND AND THE A VERAGE CPUT IME U SED TO F IND IT ,AND THE O PTIMAL L ENGTH AND THE R ELATIVE E RROR OF THE A VERAGE R ESULT WITH R ESPECT TO THE O PTIMAL SOLUTIONTABLE VIIIC OMPARISON B ETWEEN ACS-3-O PT AND STSP-GA ON TSP P ROBLEMS T AKEN FROM THEF IRST I NTERNATIONAL C ONTEST ON E VOLUTIONARY O PTIMIZATION [5].W E R EPORT THE A VERAGE L ENGTH OF THE B EST T OUR F OUND ,THE A VERAGE CPU T IME U SED TO F IND IT ,AND THE R ELATIVE E RROR WITH R ESPECT TO THE O PTIMAL S OLUTION FOR B OTH APPROACHESthe same performance as the “large step Markov chain algo-rithm”[33].This algorithm is based on a simulated annealing mechanism that uses as improvement heuristic a restricted 3-opt heuristic very similar to ours (the only difference is that they do not consider 2-opt moves)and a mutation procedure called double-bridge .(The double-bridge mutation has the property that it is the smallest change (four edges)that can not be reverted in one step by 3-opt,LK,and 2-opt.)A fair comparison of our results with the results obtained with the currently best performing algorithms for symmet-ric TSP’s [25]is difﬁcult since they use as local search a Lin–Kernighan heuristic based on a segment-tree data structure[16]that is faster and gives better results than our restricted-3-opt procedure.It will be the subject of future work to add such a procedure to the ACS.VII.D ISCUSSIONANDC ONCLUSIONSAn intuitive explanation of how the ACS works,which emerges from the experimental results presented in the preced-ing sections,is as follows.Once all the ants have generated a tour,the best ant deposits (at the end ofiterationth bit set to onewith a probability which is a function ofthebits,wherewhich isinterpreted as an integer.Cities are then ordered by increasing integer values;in case of ties the left-most city in the string5Theform of the neighborhood is given by the previous history of thesystem,that is,by pheromone accumulated on edges.comes ﬁrst in the tour.In the ACS,the pheromone matrix plays a role similar to Baluja’s generating vector,and pheromone updating has the same goal as updating the probabilities in the generating vector.Still,the two approaches are very different since in the ACS the pheromone matrix changes while ants build their solutions,while in PBIL the probability vector is modiﬁed only after a population of solutions has been generated.Moreover,the ACS uses heuristic to direct search,while PBIL does not.There are a number of ways in which the ant colony approach can be improved and/or changed.A ﬁrst possibility regards the number of ants which should contribute to the global updating rule.In ant system all the ants deposited their pheromone,while in the ACS only the best one does:obvi-ously there are intermediate possibilities.Baluja and Caruana [1]have shown that the use of the two best individuals can help PBIL to obtain better results,since the probability of being trapped in a local minimum becomes smaller.Another change to the ACS could be,again taking inspiration from [1],allowing ants which produce very bad tours to subtract pheromone.A second possibility is to move from the current parallel local updating of pheromone to a sequential one.In the ACS all ants apply the local updating rule in parallel,while they are building their tours.We could imagine a modiﬁed ACS in which ants build tours sequentially:the ﬁrst ant starts,builds its tour,and as a side effect,changes the pheromone on visited edges.Then the second ant starts and so on until the last ofthe。