上代码，少废话。本人亲测，无误。现在我贴出来。
clear all; close all;clc;%% 参数设置N=30; %麻雀个数dim=2; %评估函数维度N_discoverer=0.7*N; %发现者个数N_Followers=0.1*N; %追随者个数N_Vigilant=0.2*N; %警戒者个数Max_iter=100; %最大迭代次数ST=0.6; %安全阈值%% 测试函数f=@(x) sum(x.^2);ub=10;%边界上限lb=-10;%边界下限%% 初始化x=lb+rand(N,dim).*(ub-lb); %初始化麻雀种群for i=1:Nfitness(i)=f(x(i,:)); %计算麻雀种群的适应度值end[A,index]=sort(fitness);x_best=x(index(1),:); %记录所有麻雀走过的位置的最优位置x_worst=x(index(end),:); %记录所有麻雀走过的位置的最差位置best_fitness=A(1); %记录所有麻雀走过的位置的最优值worst_fitness=A(end); %记录所有麻雀走过的位置的最差值x_best_currently=x(index(1),:); %记录当前麻雀种群最优位置x_worst_currently=x(index(end),:); %记录当前麻雀种群最差位置best_fitness_currently=A(1); %记录当前麻雀种群最优值worst_fitness_currently=A(end); %记录当前麻雀种群最差值x_discoverer=x(index(1:N_discoverer),:); %发现者位置x_Followers=x(index(N_discoverer+1:N_discoverer+N_Followers),:); %追随者位置x_Vigilant=x(index(N_discoverer+N_Followers+1:N),:); %警戒者位置B=[-1,1];F=best_fitness; %记录每次迭代的麻雀走过的位置的最优值iter=1; %初始化迭代次数%% 开始迭代更新while iter<Max_iterfor i=1:dimC(i)=B(round(rand)+1);endA=C'*inv((C*C'));R2=rand;%更新发现者位置for i=1:N_discovererfor j=1:dimif R2<STx_discoverer(i,j)=x_discoverer(i,j)*exp(-i/rand*Max_iter);elsex_discoverer(i,j)=x_discoverer(i,j)+randn;endend%边界判断ub_flag=x_discoverer(i,:)>ub;lb_flag=x_discoverer(i,:)<lb;x_discoverer(i,:)=(x_discoverer(i,:).*(~(ub_flag+lb_flag)))+ub.*ub_flag+lb.*lb_flag;end%更新追随者位置for i=1:N_Followersfor j=1:dimif i>N/2x_Followers(i,j)=rand*exp((x_worst_currently(j)-x_Followers(i,j))/i^2);elsex_Followers(i,j)=x_discoverer(1,j)+abs(x_Followers(i,j)-x_discoverer(1,j))*A(j);endend%边界判断ub_flag=x_Followers(i,:)>ub;lb_flag=x_Followers(i,:)<lb;x_Followers(i,:)=(x_Followers(i,:).*(~(ub_flag+lb_flag)))+ub.*ub_flag+lb.*lb_flag;end%更新警戒者位置for i=1:N_Vigilantfor j=1:dimif f(x_Vigilant(i,:))~=best_fitness_currentlyx_Vigilant(i,j)=x_best_currently(j)+randn*abs(x_Vigilant(i,j)-x_best_currently(j));elsex_Vigilant(i,j)=x_Vigilant(i,j)+B(round(rand)+1)*(abs(x_Vigilant(i,j)-x_worst_currently(j)))/abs(f(x_Vigilant(i,:))-worst_fitness_currently)+1;endend%边界判断ub_flag=x_Vigilant(i,:)>ub;lb_flag=x_Vigilant(i,:)<lb;x_Vigilant(i,:)=(x_Vigilant(i,:).*(~(ub_flag+lb_flag)))+ub.*ub_flag+lb.*lb_flag;endx=[x_discoverer;x_Followers;x_Vigilant]; %得到该次迭代下的所有麻雀的新位置for i=1:Nfitness(i)=f(x(i,:)); %计算适应度end[E,index]=sort(fitness);if f(x(index(1),:))<best_fitness %更新所有麻雀走过的位置的最优位置和最优值best_fitness=f(x(index(1),:));x_best=x(index(1),:);endif f(x(index(end),:))>worst_fitness %更新所有麻雀走过的位置的最差位置和最差值worst_fitness= f(x(index(end),:));x_worst=x(index(end),:);endx_best_currently=x(index(1),:); %更新当前麻雀种群的最优位置x_worst_currently=x(index(end),:); %更新当前麻雀种群的最差位置best_fitness_currently=E(1); %更新当前麻雀的种群的最优值worst_fitness_currently=E(end); %更新当前麻雀的种群的最差值x_discoverer=x(index(1:N_discoverer),:); %重新选择种群中的发现者x_Followers=x(index(N_discoverer+1:N_discoverer+N_Followers),:); %重新选择种群中的追随者x_Vigilant=x(index(N_discoverer+N_Followers+1:N),:); %重新选择种群中的警戒者F=[F,best_fitness]; iter=iter+1; %迭代次数加一end%% 结果 作图display(['最优值是:',num2str(F(end)),'最优麻雀位置:',num2str(x_best)]);figure(1);plot(F);xlabel('迭代次数'),ylabel('适应度值');
以上代码是可以直接运行的。
复制粘贴即可
谢谢
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