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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])