MAT之PSO：利用PSO+ω参数实现对一元函数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+ω参数实现对一元函数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

实现代码

输出结果

实现代码

  

   

    

     

    
    

     

      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;
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      ws = 0.9;  
     
    
   

    

     

    
    

     

      we = 0.4;
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      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
     
    
   

    

     

    
    

     

          w = ws - (ws-we)*(i/maxgen);  
     
    
   

    

     

    
    

     

          for j = 1:sizepop
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

              V(j,:) = w*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)+ω参数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);
     
    
  
 

相关文章
PSO：利用PSO+ω参数实现对一元函数y = sin(10*pi*x) ./ x进行求解优化，找到最优个体适应度

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原文链接：yunyaniu.blog.csdn.net/article/details/79381647