TianZhendong
6 years ago
committed by
GitHub
GitHub
1 changed files with 120 additions and 0 deletions
@ -0,0 +1,120 @@ |
|||||
|
%%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') |
||||
|
|
||||
|
|
||||
|
|
||||
|
|
||||
|
|
||||
|
|
||||
Loading…
Reference in new issue