1 简介
蝗虫算法( Grasshopper Optimization Algorithm，GOA ) 是 由 Saremi 等[1]于2017 年提出的一种元启发式仿生优化算法。具体原理如下：
2 部分代码
%_________________________________________
% Multi-objective Grasshopper Optimization Algorithm (MOGOA) source codes version 1.0
%
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 grasshoppers
for iter=1:max_iter
for i=1:N
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);
end
figure
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])
img =gcf; %获取当前画图的句柄
print(img, '-dpng', '-r600', './img.png') %即可得到对应格式和期望dpi的图像
3 仿真结果
4 参考文献
[1]潘峰, and 孙红霞. "基于蝗虫算法的图像多阈值分割方法." 电子测量与仪器学报 033.001(2019):149-155.
5 MATLAB代码与数据下载地址
见博客主页
