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🏆部落客優勢：🌞🌞🌞部落格内容盡量做到思維缜密，邏輯清晰，為了友善讀者。
⛳️座右銘：行百裡者，半于九十。
目錄
💥1 概述
📚2 運作結果
🎉3 參考文獻
🌈4 Matlab代碼實作
💥1 概述
鲈魚屬于鲈形目，身體呈透明桶形。它們的組織與水母高度相似。它們的運動也與水母非常相似，其中水被泵入身體作為向前移動的推進力。
關于這種生物的生物學研究正處于早期裡程碑，主要是因為它們的生活環境極難進入，而且将它們留在實驗室環境中确實很難。論文中感興趣的是它們的蜂群行為。在深海中，貓魚通常會形成一個稱為貂魚鍊的群。這種行為的主要原因還不是很清楚，但一些研究人員認為，這樣做是為了通過快速協調的變化和覓食來實作更好的運動。
文獻中幾乎沒有對蜂群行為和鲈魚種群進行數學模組化。此外，沒有用于解決優化問題的salp群的數學模型，而蜂群，螞蟻和魚類群已被廣泛用于模組化并用于解決優化問題。Salp Swarm 算法 （SSA） 模仿 salps 來解決優化問題。
📚2 運作結果
🎉3 參考文獻
主函數代碼：
%____________________________________________________________________________________
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])