多目标鲈鱼优化算法（Matlab代码实现）

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⛳️座右铭：行百里者，半于九十。

目录

​​💥1 概述​​

​​📚2 运行结果​​

​​🎉3 参考文献​​

​​🌈4 Matlab代码实现​​

💥1 概述

     鲈鱼属于鲈形目，身体呈透明桶形。它们的组织与水母高度相似。它们的运动也与水母非常相似，其中水被泵入身体作为向前移动的推进力。

关于这种生物的生物学研究正处于早期里程碑，主要是因为它们的生活环境极难进入，而且将它们留在实验室环境中确实很难。论文中感兴趣的是它们的蜂群行为。在深海中，貓鱼通常会形成一个称为貂鱼链的群。这种行为的主要原因还不是很清楚，但一些研究人员认为，这样做是为了通过快速协调的变化和觅食来实现更好的运动。

文献中几乎没有对蜂群行为和鲈鱼种群进行数学建模。此外，没有用于解决优化问题的salp群的数学模型，而蜂群，蚂蚁和鱼类群已被广泛用于建模并用于解决优化问题。Salp Swarm 算法 （SSA） 模仿 salps 来解决优化问题。

📚2 运行结果

🎉3 参考文献

主函数代码：
 %____________________________________________________________________________________
clc;
 clear;
 close all;% Change these details with respect to your problem%%%%%%%%%%%%%%
 ObjectiveFunction=@ZDT1;
 dim=5;
 lb=0;
 ub=1;
 obj_no=2;if size(ub,2)==1
     ub=ones(1,dim)*ub;
     lb=ones(1,dim)*lb;
 end
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%max_iter=100;
 N=200;
 ArchiveMaxSize=100;Archive_X=zeros(100,dim);
 Archive_F=ones(100,obj_no)*inf;Archive_member_no=0;
r=(ub-lb)/2;
 V_max=(ub(1)-lb(1))/10;Food_fitness=inf*ones(1,obj_no);
 Food_position=zeros(dim,1);Salps_X=initialization(N,dim,ub,lb);
 fitness=zeros(N,2);V=initialization(N,dim,ub,lb);
 iter=0;position_history=zeros(N,max_iter,dim);
for iter=1:max_iter
     
     c1 = 2*exp(-(4*iter/max_iter)^2); % Eq. (3.2) in the paper
     
     for i=1:N %Calculate all the objective values first
         Salps_fitness(i,:)=ObjectiveFunction(Salps_X(:,i)');
         if dominates(Salps_fitness(i,:),Food_fitness)
             Food_fitness=Salps_fitness(i,:);
             Food_position=Salps_X(:,i);
         end
     end
     
     [Archive_X, Archive_F, Archive_member_no]=UpdateArchive(Archive_X, Archive_F, Salps_X, Salps_fitness, Archive_member_no);
     
     if Archive_member_no>ArchiveMaxSize
         Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no);
         [Archive_X, Archive_F, Archive_mem_ranks, Archive_member_no]=HandleFullArchive(Archive_X, Archive_F, Archive_member_no, Archive_mem_ranks, ArchiveMaxSize);
     else
         Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no);
     end
     
     Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no);
     % Archive_mem_ranks
     % Chose the archive member in the least population area as food`
     % to improve coverage
     index=RouletteWheelSelection(1./Archive_mem_ranks);
     if index==-1
         index=1;
     end
     Food_fitness=Archive_F(index,:);
     Food_position=Archive_X(index,:)';
     
     for i=1:N
         
         index=0;
         neighbours_no=0;
         
         if i<=N/2
             for j=1:1:dim
                 c2=rand();
                 c3=rand();
                 %%%%%%%%%%%%% % Eq. (3.1) in the paper %%%%%%%%%%%%%%
                 if c3<0.5
                     Salps_X(j,i)=Food_position(j)+c1*((ub(j)-lb(j))*c2+lb(j));
                 else
                     Salps_X(j,i)=Food_position(j)-c1*((ub(j)-lb(j))*c2+lb(j));
                 end
                 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
             end
         elseif i>N/2 && i<N+1
             
             point1=Salps_X(:,i-1);
             point2=Salps_X(:,i);
             
             Salps_X(:,i)=(point2+point1)/(2); % Eq. (3.4) in the paper
         end
         
         Flag4ub=Salps_X(:,i)>ub';
         Flag4lb=Salps_X(:,i)<lb';
         Salps_X(:,i)=(Salps_X(:,i).*(~(Flag4ub+Flag4lb)))+ub'.*Flag4ub+lb'.*Flag4lb;
         
     end
     
     display(['At the iteration ', num2str(iter), ' there are ', num2str(Archive_member_no), ' non-dominated solutions in the archive']);
     
 endfigure
Draw_ZDT1();
hold on
plot(Archive_F(:,1),Archive_F(:,2),'ro','MarkerSize',8,'markerfacecolor','k');
legend('True PF','Obtained PF');
 title('MSSA');set(gcf, 'pos', [403   466   230   200])

 

​​🌈​​4 Matlab代码实现