海洋掠食者算法(Marine Predators Algorithm，MPA)是期刊“Expert Systems With Applications ”（中科院一区IF：8.3）的2020年智能优化算法

01.引言

本文提出了一种自然启发的元启发式算法——海洋掠食者算法(MPA)及其在工程中的应用。MPA的主要灵感来自海洋捕食者普遍存在的觅食策略，即l和布朗运动，以及捕食者与猎物生物相互作用中的最优相遇率策略。海洋生态系统中捕食者和猎物之间的最佳觅食策略和相遇率策略遵循自然规律。

02.优化算法的流程

03.论文中算法对比图

04.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%------------------- Detecting top predator -----------------    
 for i=1:size(Prey,1)  
        
    Flag4ub=Prey(i,:)>ub;
    Flag4lb=Prey(i,:):)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                    
        
    fitness(i,1)=fobj(Prey(i,:));
                     
     if fitness(i,1)1); 
       Top_predator_pos=Prey(i,:);
     end          
 end
     
     %------------------- Marine Memory saving ------------------- 
    
 if Iter==0
   fit_old=fitness;    Prey_old=Prey;
 end
     
  Inx=(fit_old1,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 Iter3 
          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,:):)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
  
    fitness(i,1)=fobj(Prey(i,:));
        
      if fitness(i,1)1);
         Top_predator_pos=Prey(i,:);
      end     
  end
        
     %---------------------- Marine Memory saving ----------------
    
 if Iter==0
    fit_old=fitness;    Prey_old=Prey;
 end
     
    Inx=(fit_old1,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()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

04.本代码效果图