MAT之PSO：利用PSO算法优化二元函数，寻找最优个体适应度

实现结果

https://imgconvert.csdnimg.cn/aHR0cHM6Ly9pbWFnZXMyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTMxMzQ3NS8yMDE4MDIvMTMxMzQ3NS0yMDE4MDIyNjE5MzQwNDUyMi0xMzc5NzY0OTE5LmdpZg

设计代码

figure

[x,y] = meshgrid(-5:0.1:5,-5:0.1:5);

z = x.^2 + y.^2 - 10*cos(2*pi*x) - 10*cos(2*pi*y) + 20;

mesh(x,y,z)  

hold on

c1 = 1.49445;

c2 = 1.49445;

maxgen = 1000;  

sizepop = 100;  

Vmax = 1;

Vmin = -1;

popmax = 5;    

popmin = -5;

for i = 1:sizepop

   pop(i,:) = 5*rands(1,2);

   V(i,:) = rands(1,2);    

   fitness(i) = fun(pop(i,:));  

end

[bestfitness bestindex] = max(fitness);

zbest = pop(bestindex,:);  

gbest = pop;    

fitnessgbest = fitness;  

fitnesszbest = bestfitness;  

for i = 1:maxgen

   for j = 1:sizepop  

       V(j,:) = V(j,:) + c1*rand*(gbest(j,:) - pop(j,:)) + c2*rand*(zbest - pop(j,:));

       V(j,find(V(j,:)>Vmax)) = Vmax;

       V(j,find(V(j,:)<Vmin)) = Vmin;

       pop(j,:) = pop(j,:) + V(j,:);

       pop(j,find(pop(j,:)>popmax)) = popmax;

       pop(j,find(pop(j,:)<popmin)) = popmin;

       fitness(j) = fun(pop(j,:));

   end

   for j = 1:sizepop  

       if fitness(j) > fitnessgbest(j)

           gbest(j,:) = pop(j,:);

           fitnessgbest(j) = fitness(j);

       end

       if fitness(j) > fitnesszbest

           zbest = pop(j,:);

           fitnesszbest = fitness(j);

       end

   end

   yy(i) = fitnesszbest;            

end

[fitnesszbest, zbest]

plot3(zbest(1), zbest(2), fitnesszbest,'ro','linewidth',1.5)

title('粒子群算法：绘制的目标函数三维网格图,红圈为最优点—Jason niu')

figure

plot(yy)

title('PSO：利用粒子群算法实现对目标函数寻找最优个体适应度—Jason niu','fontsize',12);

xlabel('进化代数','fontsize',12);ylabel('适应度','fontsize',12);