MAT之PSO：利用PSO算法优化二元函数，寻找最优个体适应度

MAT之PSO：利用PSO算法优化二元函数，寻找最优个体适应度

目录

实现结果

设计代码

实现结果

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);
     
    
  
 

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PSO：利用PSO算法优化二元函数，寻找最优个体适应度

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原文链接：yunyaniu.blog.csdn.net/article/details/79380986