💥💥💥💞💞💞歡迎來到本部落格❤️❤️❤️💥💥💥
🏆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
本工作提出了一種新的多目标算法，其靈感來自自然界中蚱蜢群的導航。首先使用數學模型來模拟遊泳中個體的互相作用，包括吸引力、排斥力和舒适區。然後提出了一種機制，使用該模型在單目标搜尋空間中近似全局最優值。然後，将存檔和目标選擇技術內建到算法中，以估計多目标問題的帕累托最優前沿。為了對所提算法的性能進行基準測試，利用了一組不同的标準多目标測試問題。将結果與進化多目标優化文獻中最受好評和最新的算法進行了比較，使用三個性能名額定量和圖形定性。結果表明，所提算法在得到的帕累托最優解的精度及其分布方面能夠提供極具競争力的結果。
📚2 運作結果
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
flag=0;
if (rem(dim,2)~=0)
dim = dim+1;
ub = [ub, 1];
lb = [lb, 0];
flag=1;
end max_iter=100;
N=200;
ArchiveMaxSize=100;Archive_X=zeros(100,dim);
Archive_F=ones(100,obj_no)*inf;Archive_member_no=0;
%Initialize the positions of artificial whales
GrassHopperPositions=initialization(N,dim,ub,lb);TargetPosition=zeros(dim,1);
TargetFitness=inf*ones(1,obj_no);cMax=1;
cMin=0.00004;
%calculate the fitness of initial grasshoppersfor iter=1:max_iter
for i=1:N
Flag4ub=GrassHopperPositions(:,i)>ub';
Flag4lb=GrassHopperPositions(:,i)<lb';
GrassHopperPositions(:,i)=(GrassHopperPositions(:,i).*(~(Flag4ub+Flag4lb)))+ub'.*Flag4ub+lb'.*Flag4lb;
GrassHopperFitness(i,:)=ObjectiveFunction(GrassHopperPositions(:,i)');
if dominates(GrassHopperFitness(i,:),TargetFitness)
TargetFitness=GrassHopperFitness(i,:);
TargetPosition=GrassHopperPositions(:,i);
end
end
[Archive_X, Archive_F, Archive_member_no]=UpdateArchive(Archive_X, Archive_F, GrassHopperPositions, GrassHopperFitness, 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);
index=RouletteWheelSelection(1./Archive_mem_ranks);
if index==-1
index=1;
end
TargetFitness=Archive_F(index,:);
TargetPosition=Archive_X(index,:)';
c=cMax-iter*((cMax-cMin)/max_iter); % Eq. (3.8) in the paper
for i=1:N
temp= GrassHopperPositions;
for k=1:2:dim
S_i=zeros(2,1);
for j=1:N
if i~=j
Dist=distance(temp(k:k+1,j), temp(k:k+1,i));
r_ij_vec=(temp(k:k+1,j)-temp(k:k+1,i))/(Dist+eps);
xj_xi=2+rem(Dist,2);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.2) in the paper
s_ij=((ub(k:k+1)' - lb(k:k+1)') .*c/2)*S_func(xj_xi).*r_ij_vec;
S_i=S_i+s_ij;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end
end
S_i_total(k:k+1, :) = S_i;
end
X_new=c*S_i_total'+(TargetPosition)'; % Eq. (3.7) in the paper
GrassHopperPositions_temp(i,:)=X_new';
end
% GrassHopperPositions
GrassHopperPositions=GrassHopperPositions_temp';
display(['At the iteration ', num2str(iter), ' there are ', num2str(Archive_member_no), ' non-dominated solutions in the archive']);
end if (flag==1)
TargetPosition = TargetPosition(1:dim-1);
endfigure
Draw_ZDT1();
hold on
plot(Archive_F(:,1),Archive_F(:,2),'ro','MarkerSize',8,'markerfacecolor','k');
legend('True PF','Obtained PF');
title('MOGOA');set(gcf, 'pos', [403 466 230 200])