海洋捕食者算法（Matlab代码实现）

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目录

​​💥1 概述​​

​​📚2 运行结果​​

​​🎉3 参考文献​​

​​🌈4 Matlab代码实现​​

💥1 概述

   海洋捕食者算法（MPA）是一种自然启发的优化算法，它遵循最佳觅食策略中自然支配的规则，并在海洋生态系统中遇到捕食者和猎物之间的速率策略。

MPA优化过程分为三个主要优化阶段，考虑不同的速度比，同时模仿捕食者和猎物的整个生命周期：（1）在高速比或当猎物比捕食者移动得更快时，（2）单位速度比或当捕食者和猎物以几乎相同的速度移动时， （3）当捕食者比猎物移动得更快时，速度比低。对于每个定义的阶段，指定并分配特定的迭代周期。这些步骤是根据捕食者和猎物运动性质的规则定义的，同时模仿自然界中捕食者和猎物的运动。

📚2 运行结果

部分代码：

function [Top_predator_fit,Top_predator_pos,Convergence_curve]=MPA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
 Top_predator_pos=zeros(1,dim);
 Top_predator_fit=inf; Convergence_curve=zeros(1,Max_iter);
 stepsize=zeros(SearchAgents_no,dim);
 fitness=inf(SearchAgents_no,1); Prey=initialization(SearchAgents_no,dim,ub,lb);
   
 Xmin=repmat(ones(1,dim).*lb,SearchAgents_no,1);
 Xmax=repmat(ones(1,dim).*ub,SearchAgents_no,1);
          Iter=0;
 FADs=0.2;
 P=0.5;while Iter<Max_iter    
      %------------------- Detecting top predator -----------------    
  for i=1:size(Prey,1)  
         
     Flag4ub=Prey(i,:)>ub;
     Flag4lb=Prey(i,:)<lb;    
     Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                    
         
     fitness(i,1)=fobj(Prey(i,:));
                      
      if fitness(i,1)<Top_predator_fit 
        Top_predator_fit=fitness(i,1); 
        Top_predator_pos=Prey(i,:);
      end          
  end
      
      %------------------- Marine Memory saving ------------------- 
     
  if Iter==0
    fit_old=fitness;    Prey_old=Prey;
  end
      
   Inx=(fit_old<fitness);
   Indx=repmat(Inx,1,dim);
   Prey=Indx.*Prey_old+~Indx.*Prey;
   fitness=Inx.*fit_old+~Inx.*fitness;
         
   fit_old=fitness;    Prey_old=Prey;     %------------------------------------------------------------   
      
  Elite=repmat(Top_predator_pos,SearchAgents_no,1);  %(Eq. 10) 
  CF=(1-Iter/Max_iter)^(2*Iter/Max_iter);
                              
  RL=0.05*levy(SearchAgents_no,dim,1.5);   %Levy random number vector
  RB=randn(SearchAgents_no,dim);          %Brownian random number vector
            
   for i=1:size(Prey,1)
      for j=1:size(Prey,2)        
        R=rand();
           %------------------ Phase 1 (Eq.12) ------------------- 
        if Iter<Max_iter/3 
           stepsize(i,j)=RB(i,j)*(Elite(i,j)-RB(i,j)*Prey(i,j));                    
           Prey(i,j)=Prey(i,j)+P*R*stepsize(i,j); 
              
           %--------------- Phase 2 (Eqs. 13 & 14)----------------
        elseif Iter>Max_iter/3 && Iter<2*Max_iter/3 
           
          if i>size(Prey,1)/2
             stepsize(i,j)=RB(i,j)*(RB(i,j)*Elite(i,j)-Prey(i,j));
             Prey(i,j)=Elite(i,j)+P*CF*stepsize(i,j); 
          else
             stepsize(i,j)=RL(i,j)*(Elite(i,j)-RL(i,j)*Prey(i,j));                     
             Prey(i,j)=Prey(i,j)+P*R*stepsize(i,j);  
          end  
          
          %----------------- Phase 3 (Eq. 15)-------------------
        else 
            
            stepsize(i,j)=RL(i,j)*(RL(i,j)*Elite(i,j)-Prey(i,j)); 
            Prey(i,j)=Elite(i,j)+P*CF*stepsize(i,j);  
     
        end  
       end                                         
   end    
         
      %------------------ Detecting top predator ------------------        
   for i=1:size(Prey,1)  
         
     Flag4ub=Prey(i,:)>ub;  
     Flag4lb=Prey(i,:)<lb;  
     Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
   
     fitness(i,1)=fobj(Prey(i,:));
         
       if fitness(i,1)<Top_predator_fit 
          Top_predator_fit=fitness(i,1);
          Top_predator_pos=Prey(i,:);
       end     
   end
         
      %---------------------- Marine Memory saving ----------------
     
  if Iter==0
     fit_old=fitness;    Prey_old=Prey;
  end
      
     Inx=(fit_old<fitness);
     Indx=repmat(Inx,1,dim);
     Prey=Indx.*Prey_old+~Indx.*Prey;
     fitness=Inx.*fit_old+~Inx.*fitness;
         
     fit_old=fitness;    Prey_old=Prey;     %---------- Eddy formation and FADs� effect (Eq 16) ----------- 
                              
   if rand()<FADs
      U=rand(SearchAgents_no,dim)<FADs;                                                                                              
      Prey=Prey+CF*((Xmin+rand(SearchAgents_no,dim).*(Xmax-Xmin)).*U);  else
      r=rand();  Rs=size(Prey,1);
      stepsize=(FADs*(1-r)+r)*(Prey(randperm(Rs),:)-Prey(randperm(Rs),:));
      Prey=Prey+stepsize;
   end
                                                         
   Iter=Iter+1;  
   Convergence_curve(Iter)=Top_predator_fit; 
        
 end

🎉3 参考文献

 A. Faramarzi, M. Heidarinejad, S. Mirjalili, A.H. Gandomi,  Marine Predators Algorithm: A Nature-inspired Metaheuristic Expert Systems with Applications DOI: doi.org/10.1016/j.eswa.2020.113377

​​🌈​​4 Matlab代码实现