MAT之PSO：利用PSO实现对一元函数y = sin(10*pi*x) ./ x进行求解优化，找到最优个体适应度

MAT之PSO：利用PSO实现对一元函数y = sin(10*pi*x) ./ x进行求解优化，找到最优个体适应度

目录

输出结果

代码设计

输出结果

代码设计

  

   

    

     

    
    

     

      x = 1:0.01:2; 
     
    
   

    

     

    
    

     

      y = sin(10*pi*x) ./ x;  
     
    
   

    

     

    
    

     

      figure
     
    
   

    

     

    
    

     

      plot(x, y)
     
    
   

    

     

    
    

     

      title('绘制目标函数曲线图—Jason niu');
     
    
   

    

     

    
    

     

      hold on
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      c1 = 1.49445; 
     
    
   

    

     

    
    

     

      c2 = 1.49445;
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      maxgen = 50; 
     
    
   

    

     

    
    

     

      sizepop = 10;  
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      Vmax = 0.5; 
     
    
   

    

     

    
    

     

      Vmin = -0.5;
     
    
   

    

     

    
    

     

      popmax = 2; 
     
    
   

    

     

    
    

     

      popmin = 1;
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      for i = 1:sizepop
     
    
   

    

     

    
    

     

       pop(i,:) = (rands(1) + 1) / 2 + 1; 
     
    
   

    

     

    
    

     

       V(i,:) = 0.5 * rands(1);  
     
    
   

    

     

    
    

     

       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]
     
    
   

    

     

    
    

     

      plot(zbest, fitnesszbest,'r*')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      figure
     
    
   

    

     

    
    

     

      plot(yy)
     
    
   

    

     

    
    

     

      title('PSO：PSO算法(快于GA算法)实现找到最优个体适应度—Jason niu','fontsize',12);
     
    
   

    

     

    
    

     

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

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原文链接：yunyaniu.blog.csdn.net/article/details/79379034