matlab算法解析实现 - 基于动态粒子群算法的动态环境寻优算法.rar

%% 动态粒子群算法 %% 清空环境 clear clc %% 设置双峰参数 % 设置con1参数 X1 = 25; Y1 = 25; H1 =410; %设置con2参数 H2=zeros(1,1200); i=1:200; H2(i)=450-fix(i/5); i=201:700; H2(i)=410; i=701:1000; H2(i)=350 + fix((i-500)/10)*3; i=1001:1200; H2(i) = 500; X2=zeros(1,1200); i=1:1200; Y2(i)=-25; i=1:500; X2(i)=-25 + (i-1)*25/500; i=501:1000; X2(i)=0; i=1001:1200; X2(i)=(i-1000)*25/200; %% 初始化粒子和敏感粒子 % 种群规模 n = 20; % 粒子和敏感粒子 pop = unidrnd(501,[n,2]); popTest = unidrnd(501,[5*n,2]); % 数字高度 h = DF1function(X1,Y1,H1,X2(1),Y2(1),H2(1)); V = unidrnd(100,[n,2])-50; Vmax=25;Vmin=-25; %% 粒子和敏感粒子适应度值 fitness=zeros(1,n); fitnessTest=zeros(1,n); for i=1:n fitness(i)=h(pop(i,1),pop(i,2)); fitnessTest(i)=h(popTest(i,1),popTest(i,2)); end oFitness=sum(fitnessTest); %敏感粒子 [value,index]=max(fitness); popgbest=pop; popzbest=pop(index,:); fitnessgbest=fitness; fitnesszbest=fitness(index); %% 算法参数 vmax = 10; vmin = -10; popMax=501; popMin=1; m = 2; nFitness = oFitness; Tmax=100; %每次迭代次数 fitnessRecord=zeros(1,1200); %% 循环寻找最优点 for k = 1:1200 k % 新数字地图 h = DF1function(X1,Y1,H1,X2(k),Y2(k),H2(k)); % 敏感粒子变化 for i=1:5*n fitnessTest(i)=h(popTest(i,1),popTest(i,2)); end oFitness=sum(fitnessTest); % 变化超过阈值，重新初始化 if abs(oFitness - nFitness)>1 index=randperm(20); pop(index(1:10),:)=unidrnd(501,[10,2]); V(index(1:10),:)=unidrnd(100,[10,2])-50; end % 粒子搜索 for i=1:Tmax for j=1:n % 速度更新 V(j,:)=V(j,:)+floor(rand*(popgbest(j,:)-pop(j,:)))+floor(rand*(popzbest - pop(j,:))); index1=find(V(j,:)>Vmax); V(j,index1)=Vmax; index2=find(V(j,:)<Vmin); V(j,index2)=Vmin; % 个体更新 pop(j,:)=pop(j,:)+V(j,:); index1=find(pop(j,:)>popMax); pop(j,index1)=popMax; index2=find(pop(j,:)<popMin); pop(j,index2)=popMin; % 新适应度值 fitness(j)=h(pop(j,1),pop(j,2)); % 个体极值更新 if fitness(j) > fitnessgbest(j) popgbest(j,:) = pop(j,:); fitnessgbest(j) = fitness(j); end % 群体极值更新 if fitness(j) > fitnesszbest popzbest= pop(j,:); fitnesszbest = fitness(j); end end end fitnessRecord(k)=fitnesszbest; fitnesszbest=0; fitnessgbest=zeros(1,20); end figure plot(fitnessRecord(1:1200),'*-') title('动态最优值')