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%%清空环境
clc;
clear;
close all;
%% 参数设置
ChromosomeSize = 1; %染色体个数
ChromosomeLen=17; %染色体长度 由最大值决定
PopulationSize = 200; %种群规模
MaxIter = 200; %最大迭代次数
% MinFitness=0.01; %最小适应值
CrossRate=0.6; %交叉概率
MutateRate=0.1; %变异概率
% ObjFun=@PSO_PID; %适应值函数
NoChangeNo=5;
% UpLimit=30; %上限
%% 初始化种群init.m
Population1=rand(PopulationSize,ChromosomeLen); %种群,预分配内存
% Population2=rand(PopulationSize,ChromosomeLen); %种群,预分配内存
% Population3=rand(PopulationSize,ChromosomeLen); %种群,预分配内存
for i=1:PopulationSize
disp(['正在初始化种群',num2str(i)]);
for j=1:ChromosomeLen
Population1(i,j)=round(rand);
end
end
% for i=1:PopulationSize
% disp(['正在初始化种群',num2str(i)]);
% for j=1:ChromosomeLen
% Population2(i,j)=round(rand);
% end
% end
% for i=1:PopulationSize
% disp(['正在初始化种群',num2str(i)]);
% for j=1:ChromosomeLen
% Population3(i,j)=round(rand);
% end
% end
clear i;
clear j;
%% 开始循环
%Iter =1;
PopulationFitness=zeros(PopulationSize,1); %种群适应度值,预分配内存
BestFitness=zeros(MaxIter,1); %初始化每一代的最佳适应度
AveFitness=zeros(MaxIter,1); %初始化每一代的平均适应度
Elite1=zeros(MaxIter,1); %用于记录每一代的最优解
Elite2=zeros(MaxIter,1); %用于记录每一代的最优解
Elite3=zeros(MaxIter,1); %用于记录每一代的最优解
for Iter=1:MaxIter
disp(['迭代次数:',num2str(Iter)]); %显示迭代进度
%% 适应值计算Fitness
for i=1:PopulationSize
PopulationFitness(i,1) = fitness(decode(Population1(i,:)));
% PopulationFitness(i,1) = fitness(decode(Population1(i,:)),decode(Population2(i,:)),decode(Population3(i,:)));
end
%% 适应值大小排序,并保存最佳个体和最佳适应度
FitnessSum=sum(PopulationFitness); %种群累加适应度
AveFitness(Iter,1)=FitnessSum/PopulationSize; %种群平均适应度
[PopulationFitness,Index]=sort(PopulationFitness); %适应值从小到大排序
BestFitness(Iter,1) = PopulationFitness(PopulationSize,1); %记录每一代的最佳适应度
Elite1(Iter,1) = decode(Population1(Index(PopulationSize),:)); %记录本代的精英
% Elite2(Iter,1) = decode(Population2(Index(PopulationSize),:)); %记录本代的精英
% Elite3(Iter,1) = decode(Population3(Index(PopulationSize),:)); %记录本代的精英
disp(['最佳适应度:',num2str(BestFitness(Iter,1))]);
disp(['最佳个体:',num2str(Elite1(Iter,1)),' ',num2str(Elite2(Iter,1)),' ',num2str(Elite3(Iter,1))]);
%% 复制适应值最大的不变的染色体
PopulationNew1=zeros(PopulationSize,ChromosomeLen); %初始化新的种群
% PopulationNew2=zeros(PopulationSize,ChromosomeLen); %初始化新的种群
% PopulationNew3=zeros(PopulationSize,ChromosomeLen); %初始化新的种群
for i=1:NoChangeNo
PopulationNew1(i,:)=Population1(Index(PopulationSize-i+1),:);
% PopulationNew2(i,:)=Population2(Index(PopulationSize-i+1),:);
% PopulationNew3(i,:)=Population3(Index(PopulationSize-i+1),:);
end
%% 轮盘赌法 选择selection
for i=(NoChangeNo+1):2:PopulationSize
[idx1,idx2] = selection(PopulationSize,FitnessSum,PopulationFitness,Index);
%% 父母交叉形成子代
Rate=rand;
if Rate<=CrossRate
acr_position = floor(ChromosomeLen*rand+1); %交叉节点
[PopulationNew1(i,:),PopulationNew1(i+1,:)]=crossover(acr_position,Population1(idx1,:),Population1(idx2,:));
% [PopulationNew2(i,:),PopulationNew2(i+1,:)]=crossover(acr_position,Population2(idx1,:),Population2(idx2,:));
% [PopulationNew3(i,:),PopulationNew3(i+1,:)]=crossover(acr_position,Population3(idx1,:),Population3(idx2,:));
end
end
%% 变异
for i=(NoChangeNo+1):PopulationSize
PopulationNew1(i,:)=mutation(ChromosomeLen,MutateRate,PopulationNew1(i,:));
% PopulationNew2(i,:)=mutation(ChromosomeLen,MutateRate,PopulationNew2(i,:));
% PopulationNew3(i,:)=mutation(ChromosomeLen,MutateRate,PopulationNew3(i,:));
end
parfor i=1:PopulationSize
Population1(i,:)=PopulationNew1(i,:);
% Population2(i,:)=PopulationNew2(i,:);
% Population3(i,:)=PopulationNew3(i,:);
end
% K_p(1,Iter)=Elite1(Iter,1);
% K_i(1,Iter)=Elite2(Iter,1);
% K_d(1,Iter)=Elite3(Iter,1);
end
% figure(1)
% plot(BestFitness,'LineWidth',2);
%
% figure(2)
% plot(K_p)
% hold on
% plot(K_i,'k','LineWidth',3)
% plot(K_d,'--r')