DE，JADE源代码（matlab）.zip_DE进化算法_JADE代码_JADE进化算法_PME_jade 算法

function JADE() clear all; clc; format long; Ucr=0.5;Uf=0.5;A=[]; Gm = 10000;%最大迭代次数 Np = 100;%种群大小 D=10;%维数 xmin = -100;%最小下界 xmax = 100;%最大上界 ps=.5; c=.8; value = zeros(1,Np);%适应度值 value_1=zeros(1,Np);%变异交叉后的适应度值 Gmin = zeros(1,Gm);%每一代的最优值 best_x = zeros(Gm,D);%每一代的最优解 pop_next_1=zeros(Np,D);%变异交叉后的种群 pop_next=zeros(Np,D);%选择后的下一代种群 function y = f(v) %Rastrigr 函数 %y = sum(v.^2 - 10.*cos(2.*pi.*v) + 10); y=sum(v.^2); end pop=rand(Np,D)*(xmax-xmin)+xmin;%初始化种群 for g=1:Gm g Sf=zeros(1,Np);%每个个体的缩放因子（变异因子） Scr=zeros(1,Np);%每个个体的交叉因子 for i=1:Np %%%%%%%%%%%%%%%%%%%%%%%%----变异操作----%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% CRi=normrnd(Ucr, sqrt(0.1));%正态分布 % Fi=randc(Uf,0.1); Fi= Uf + 0.1 * tan(pi * (rand - 0.5));%柯西分布 for m=1:Np%求适应度值 value(m)=f(pop(m,:)); end [a,b]=sort(value,2);%排序 Pbest=b(round(randi(Np*ps)));%取较好解的前%ps中的一个解 dx = randperm(100); r1=dx(1);%另外三个向量 Ig=dx(2); %if r1 == i % r1= dx(3); %else % if Ig == i % Ig = dx(3); % end %end pop_1=[pop;A]; r2g=round(randi(size(pop_1,1))); son=pop(Ig,:)+ Fi*(pop(Pbest,:)-pop(Ig,:))+Fi*(pop(r1,:)-pop_1(r2g,:));%变异 for j = 1: D%边界处理 while(son(1,j)<xmin) son(1,j) =(son(1,j)+xmin)/2; end while(son(1,j)>xmax) son(1,j) =(son(1,j)+xmax)/2; end end %%%%%%%%%%%%%%%%%%%%%%%---交叉操作----%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% jrand=randi(D); for n=1:D if jrand==n||rand<CRi pop_next_1(i,n)=son(1,n); else pop_next_1(i,n)=pop(i,n); end end %%%%%%%%%%%%%%%%%%----选择操作---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if f(pop(i,:))<=f(pop_next_1(i,:)) pop_next(i,:)=pop(i,:); else pop_next(i,:)=pop_next_1(i,:); A=[A;pop(i,:)];%存储较差的解 Scr(1,i)=CRi;%较好的交叉因子 Sf(1,i)=Fi;%较好的变异因子 end end for m = 1:Np value_1(m) = f(pop_next(m,:)); end [value_min,pos_min] = min(value_1);%求每一代的最优解 Gmin(g)=value_min;%存储每一代的最优适应度值 best_x(g,:)=pop_next(pos_min,:);%存储每一代的最优适解 trace(g,1) = g; trace(g,2) = value_min; a=size(A,1);%A大于种群大小，随机删除一些不良解 if a>Np %A(1:Np,:)=A(randperm(a, Np),:); %A(Np+1:a,:)=[]; A(1:Np,:)= A(randperm(a, Np),:); A(Np+1:a,:)=[]; end num_1=length(find(Scr(1,:)~=0)); meanA=sum(Scr)/num_1; Ucr=(1-c)*Ucr+c*meanA;%调整交叉因子 L=sum(Sf); %if abs(c-0)<1.0E-14 %break; %end meanL=sum(Sf.^2)/(L+1.0E-14); Uf=(1-c)*Uf+c*meanL;%调整变异因子 pop=pop_next; end [value_min,pos_min]=min(Gmin);%求全局最优值 pos_min best_value = value_min best_vector = best_x(pos_min,:)%全局最优解 plot(trace(:,1),trace(:,2)); end