粒子群算法的matlab代码实现

粒子群算法的matlab代码实现

function swarm

warning off MATLAB:divideByZero

%%% Script Particle Swarm Optimization

%%% Author: Ivan Candelas del Toro

%%% e-mail: ivanct@https://www.360docs.net/doc/ac1739454.html,

%%%%%

% Control variables

%%%%%

global numberOfParticles;

numberOfParticles = 40;

global numberOfNeighbors;

numberOfNeighbors = 4;

maxIterations = 1000;

%% Limites para cambio de localización

global deltaMin;

deltaMin = -4.0;

global deltaMax;

deltaMax = 4.0;

%% individuality and sociality

iWeight = 2.0;

iMin = 0.0;

iMax = 1.0;

sWeight = 2.0;

sMin = 0.0;

sMax = 1.0;

%%%%%%%%%%%

%% Related variables to the problem space solutions %%%%%%%%%%%

initialFitness = -100000;

targetFitness = 0;

global dimensions;

dimensions = 4;

%%%% Program Start

global particles;

for p = 1:numberOfParticles

for d = 1:dimensions

particles(p).next(d) = randint(1,10); %%%%%%%%%%%%%%%%%

particles(p).velocity(d) =

randint(deltaMin,deltaMax); %%%%%%%%%%%

particles(p).current(d) = particles(p).next(d);

end

particles(p).bestSoFar = initialFitness;

end

for p = 1:numberOfParticles

neighborVector = getNeighborIndexVector(p,numberOfNeighbors);

for n = 1:numberOfNeighbors

particles(p).neighbor(n) = neighborVector(n);

end

end

iterations = 0;

tic

while iterations <= maxIterations

for p = 1:numberOfParticles

for d = 1:dimensions

particles(p).current(d) = particles(p).next(d);

end

fitness = test(p);

if fitness > particles(p).bestSoFar;

particles(p).bestSoFar = fitness;

for d = 1:dimensions

particles(p).best(d) = particles(p).current(d);

end

end

if fitness == targetFitness

X=particles(p).current(1)

Y=particles(p).current(2)

Z=particles(p).current(3)

W=particles(p).current(4)

total_time=toc

disp('Success!!');

return

end

end

for p = 1:numberOfParticles

n = getNeighborIndexWithBestFitness(p);

for d = 1:dimensions

iFactor = iWeight * randint(iMin,iMax); %%%%%%%%%%%%%%

sFactor = sWeight * randint(sMin,sMax); %%%%%%%%%%%%%

pDelta(d) = particles(p).best(d) - particles(p).current(d); nDelta(d) = particles(n).best(d) - particles(p).current(d);

delta = (iFactor * pDelta(d)) + (sFactor * nDelta(d));

delta = particles(p).velocity(d) + delta;

particles(p).velocity(d) = constrict(delta);

particles(p).next(d) = particles(p).current(d) +

particles(p).velocity(d);

end

end

iterations = iterations + 1

end

disp('Failure');

%%%%%%%%%%

%%% Support Functions

%%%%%%%%%%

%%-------------

function fitness = test(p)

global particles;

x = particles(p).current(1);

y = particles(p).current(2);

z = particles(p).current(3);

w = particles(p).current(4);

f = 5 * (x^2) + 2 * (y^3) - (z/w)^2 + 4;

if ( x * y ) == 0

n = 1;

else

n = 0;

end

fitness = 0 - abs(f) - n;

%%-------------

function d = constrict(delta)

global deltaMin;

global deltaMax;

if delta < deltaMin

d = deltaMin;

return

end

if delta > deltaMax

d = deltaMax;

return

end

d = delta;

%%--------

function p = getNeighborIndex(pindex,n)

global numberOfParticles;

global dimensions;

global particles;

dista=zeros(1,numberOfParticles);

for i = 1:numberOfParticles

suma = 0;

for d = 1:dimensions

suma = suma + (particles(pindex).current(d) - particles(i).current(d))^2;

end

dista(i)=sqrt(suma);

end

[X,I] = sort(dista);

p = I(n);

%%--------

function p = getNeighborIndexVector(pindex,n) global numberOfParticles;

global dimensions;

global particles;

dista=zeros(1,numberOfParticles);

for i = 1:numberOfParticles

suma = 0;

for d = 1:dimensions

suma = suma + (particles(pindex).current(d) - particles(i).current(d))^2;

end

dista(i)=sqrt(suma);

end

[X,I] = sort(dista);

p = I(1:n);

%%---------

function p = getNeighborIndexWithBestFitness(pindex) global dimensions;

global particles;

global numberOfNeighbors;

fit = zeros(1,4);

for d = 1:numberOfNeighbors;

fit(d) = test(particles(pindex).neighbor(d));

end

[X,I] = sort(fit);

p = particles(pindex).neighbor(I(1));

%%-------------

%

%return a random integer between min and max

%

function num = randint(min,max)

array = min:max;

index = mod(floor(rand(1)*1000),size(array,2))+1; num = array(index);