3 仿真结果
function MOFA_MOCS_ZDT1 %多策略协同多目标萤火虫算法 %Programmed by Kevin Kong
%测试问题ZDT-1 clc; global NP N T_MAX gamma beta0 epsilon M V NP = 100;%
种群大小 T_MAX = 500;%最大迭代次数 N = 100;%外部档案规模 gamma = 1;%光吸收系数
beta0 = 1;%最大吸引力 M = 2;%目标函数个数 V = 30;%决策变量个数 t = 1;%迭代
次数 epsilon = get_epsilon(); %变量范围在[0,1] min_range = zeros(1,V);
max_range = ones(1,V); pop = init(NP,M,V,min_range,max_range);%初始化种群
Arc = pop(non_domination_sort(pop,M,V),:);%非支配排序 while(t <= T_MAX)
plot(pop(:,V+1),pop(:,V+2),'*'); str = sprintf('第%d代',t);
title(str); drawnow; offspring = pop;%子代 for i = 1:NP
for j = 1:NP domination =
get_domination(pop(i,:),pop(j,:),M,V); if(domination ~= -1)
%i和j之间存在支配关系 g =
Arc(1+fix((size(Arc,1)-1)*rand(1)),:);%从Arc里随机选取一个个体作为g*
if(domination == 0) %i支配j
offspring(j,1:V) = firefly_move(pop(i,:),pop(j,:),V,beta0,gamma,true,g);
offspring(j,1:V) =
outbound(offspring(j,1:V),V,min_range,max_range); else
%j支配i offspring(i,1:V) =
firefly_move(pop(j,:),pop(i,:),V,beta0,gamma,true,g);
offspring(i,1:V) = outbound(offspring(i,1:V),V,min_range,max_range);
end else %i和j之间不存在支配关系
g = Arc(1+fix((size(Arc,1)-1)*rand(1)),:);%从Arc里随机选取一个个体作为g*
res = firefly_move(pop(i,:),pop(j,:),V,beta0,gamma,false,g);
offspring(i,1:V) = res(1,:); offspring(i,1:V) =
outbound(offspring(i,1:V),V,min_range,max_range);
offspring(j,1:V) = res(2,:); offspring(j,1:V) =
outbound(offspring(j,1:V),V,min_range,max_range); end
end end pop = offspring;%更新萤火虫位置 for i = 1:N
pop(i,V+1:V+M) = evaluate_objective(pop(i,:));%评估萤火虫个体 end Arc
= update_Arc(pop,Arc,N,M,V,epsilon);%利用ε-三点最短路径方法维持Arc档案 t = t +
1; endend%% function f = init(N,M,V,min,max) %初始化种群，随机生成个体并计算其适
度值 %N:种群大小 %M:目标函数数量 %V:决策变量数 %min:变量范围下限 %max：变
量范围上限 f = [];%存放个体和目标函数值,1:V是决策变量，V+1:V+2是目标函数值 for j =
1:V delta(j) = (max(j) - min(j))/N;%将决策变量x(j)的区间均匀划分成N等分;
lamda = min(j):delta(j):max(j);%得到N个子区间 for i = 1:N %从N个子
区间中随机选择一个 [~,n] = size(lamda);%获得子区间个数n rand_n =
1 + fix((n-2)*rand(1));%随机位置 min_range = lamda(rand_n);%获得子区间的
下限 max_range = lamda(rand_n+1);%获得子区间的上限 f(i,j) =
min_range + (max_range - min_range)*rand(1);%随机生成 lamda(rand_n) =
[];%删除该子区间 end end %计算个体的适度值 for i = 1:N
f(i,V+1:V+M) = evaluate_objective(f(i,:));%计算目标函数值 endend%%function f =
evaluate_objective(x) %根据目标函数计算适度值，测试方法：ZDT-1 global V f =
[]; f(1) = x(1);%目标函数1 g = 1; g_tmp = 0; for i = 2:V g_tmp
= g_tmp + x(i); end g = g + 9*g_tmp/(V-1); f(2) = g*(1-sqrt(x(1)/g));%目
标函数2end%%
