基于进化状态判定的模糊自适应二进制粒子群优化算法

基于进化状态判定的

模糊自适应二进制粒子群优化算法

李浩君1张征1张鹏威1王万良2

摘要随着迭代过程的推进,二进制粒子群算法容易陷入局部最优解,后期收敛性较差.针对此缺点,文中提出基于进化状态判定的模糊自适应二进制粒子群优化算法.采用隶属函数进行模糊分类的方法,判定种群进化状态.在迭代过程前期采用S形映射函数和较大的惯性权重值,提高收敛速度,保证算法的稳定性.后期采用V形映射函数和动态增减的惯性权重值,增强算法后期全局探索能力,避免其陷入局部最优.仿真实验表明,文中算法的收敛速度较快,精度较高,搜索能力较好,可以避免早熟现象.

关键词二进制粒子群算法,进化状态,模糊分类,隶属函数

引用格式李浩君,张征,张鹏威,王万良.基于进化状态判定的模糊自适应二进制粒子群优化算法.模式识别与人工智能,2018,31(4):358-369.

DOI10.16451/https://www.360docs.net/doc/c77786864.html,ki.issn1003-6059.201804007中图法分类号TP18

Fuzzy Adaptive Binary Particle Swarm Optimization Algorithm Based on

Evolutionary State Determination

LI Haojun1,ZHANG Zheng1,ZHANG Pengwei1,WANG Wanliang2 ABSTRACT Since the binary particle swarm algorithm is easy to fall into local optimal solution and its convergence performance during later period is poor,a fuzzy adaptive binary particle swarm optimization algorithm based on evolutionary state determination(EFBPSO)is proposed.Population evolution state is determined by fuzzy classification method based on membership function.S-shaped mapping function and large inertia weight value are adopted to improve convergence speed and ensure stability of the algorithm in the earlier stage of the iterative process.V-shaped mapping function and the smaller inertia weight are employed to enhance global exploration ability of the algorithm and avoid the algorithm falling into local optimization in the later stage of iterative process.Simulation experimental results show that EFBPSO possesses higher convergence speed and accuracy and obtains better searching ability to avoid prematurity. Key Words Binary Particle Swarm Optimization,Evolutionary State,Fuzzy Classification,Member-ship Function

Citation LI H J,ZHANG Z,ZHANG P W,WANG W L.Fuzzy Adaptive Binary Particle Swarm Optimization Algorithm Based on Evolutionary State Determination.Pattern Recognition and

Artificial Intelligence,2018,31(4):358-369.

收稿日期:2017-12-01;录用日期:2018-02-27 Manuscript received December1,2017; accepted February27,2018

国家自然科学基金项目(No.61503340)二国家社会科学基金项目(No.16BTQ084)资助

Supported by National Natural Science Foundation of China(No. 61503340),National Social Science Foundation of China(No. 16BTQ084)本文责任编委付俊Recommended by Associate Editor FU Jun

1.浙江工业大学教育科学与技术学院杭州310023

2.浙江工业大学计算机科学与技术学院杭州310023 1.College of Education,Zhejiang University of Technology,Hang-zhou310023

2.College of Computer Science and Technology,Zhejiang Uni-versity of Technology,Hangzhou310023

第31卷第4期模式识别与人工智能Vol.31 No.4 2018年4月Pattern Recognition and Artificial Intelligence Apr.2018

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