TianZhendong
6 years ago
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1 changed files with 120 additions and 0 deletions
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%%清空环境 |
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clc; |
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clear; |
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close all; |
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%% 参数设置 |
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ChromosomeSize = 1; %染色体个数 |
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ChromosomeLen=17; %染色体长度 由最大值决定 |
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PopulationSize = 200; %种群规模 |
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MaxIter = 200; %最大迭代次数 |
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% MinFitness=0.01; %最小适应值 |
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CrossRate=0.6; %交叉概率 |
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MutateRate=0.1; %变异概率 |
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% ObjFun=@PSO_PID; %适应值函数 |
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NoChangeNo=5; |
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% UpLimit=30; %上限 |
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global Kp; |
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global Ki; |
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global Kd; |
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%% 初始化种群init.m |
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Population1=rand(PopulationSize,ChromosomeLen); %种群,预分配内存 |
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Population2=rand(PopulationSize,ChromosomeLen); %种群,预分配内存 |
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Population3=rand(PopulationSize,ChromosomeLen); %种群,预分配内存 |
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for i=1:PopulationSize |
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disp(['正在初始化种群',num2str(i)]); |
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for j=1:ChromosomeLen |
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Population1(i,j)=round(rand); |
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end |
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end |
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for i=1:PopulationSize |
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disp(['正在初始化种群',num2str(i)]); |
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for j=1:ChromosomeLen |
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Population2(i,j)=round(rand); |
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end |
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end |
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for i=1:PopulationSize |
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disp(['正在初始化种群',num2str(i)]); |
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for j=1:ChromosomeLen |
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Population3(i,j)=round(rand); |
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end |
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end |
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clear i; |
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clear j; |
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%% 开始循环 |
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PopulationFitness=zeros(PopulationSize,1); %种群适应度值，预分配内存 |
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BestFitness=zeros(MaxIter,1); %初始化每一代的最佳适应度 |
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AveFitness=zeros(MaxIter,1); %初始化每一代的平均适应度 |
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K_p=zeros(MaxIter,1); %初始化 用于提高运算速度 |
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K_i=zeros(MaxIter,1); %初始化 用于提高运算速度 |
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K_d=zeros(MaxIter,1); %初始化 用于提高运算速度 |
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Elite1=zeros(MaxIter,1); %用于记录每一代的最优解 |
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Elite2=zeros(MaxIter,1); %用于记录每一代的最优解 |
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Elite3=zeros(MaxIter,1); %用于记录每一代的最优解 |
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for Iter=1:MaxIter |
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disp(['迭代次数：',num2str(Iter)]); %显示迭代进度 |
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%% 适应值计算Fitness |
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for i=1:PopulationSize |
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% PopulationFitness(i,1) = fitness(decode(Population1(i,:))); |
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Kp=decode(Population1(i,:)); |
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Ki=decode(Population2(i,:)); |
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Kd=decode(Population3(i,:)); |
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PopulationFitness(i,1) = fitness(Kp,Ki,Kd); |
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end |
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%% 适应值大小排序，并保存最佳个体和最佳适应度 |
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FitnessSum=sum(PopulationFitness); %种群累加适应度 |
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AveFitness(Iter,1)=FitnessSum/PopulationSize; %种群平均适应度 |
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[PopulationFitness,Index]=sort(PopulationFitness); %适应值从小到大排序 |
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BestFitness(Iter,1) = PopulationFitness(PopulationSize,1); %记录每一代的最佳适应度 |
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Elite1(Iter,1) = decode(Population1(Index(PopulationSize),:)); %记录本代的精英 |
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Elite2(Iter,1) = decode(Population2(Index(PopulationSize),:)); %记录本代的精英 |
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Elite3(Iter,1) = decode(Population3(Index(PopulationSize),:)); %记录本代的精英 |
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disp(['最佳适应度：',num2str(BestFitness(Iter,1))]); |
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disp(['最佳个体：',num2str(Elite1(Iter,1)),' ',num2str(Elite2(Iter,1)),' ',num2str(Elite3(Iter,1))]); |
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%% 复制适应值最大的不变的染色体 |
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PopulationNew1=zeros(PopulationSize,ChromosomeLen); %初始化新的种群 |
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PopulationNew2=zeros(PopulationSize,ChromosomeLen); %初始化新的种群 |
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PopulationNew3=zeros(PopulationSize,ChromosomeLen); %初始化新的种群 |
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for i=1:NoChangeNo |
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PopulationNew1(i,:)=Population1(Index(PopulationSize-i+1),:); |
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PopulationNew2(i,:)=Population2(Index(PopulationSize-i+1),:); |
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PopulationNew3(i,:)=Population3(Index(PopulationSize-i+1),:); |
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end |
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%% 轮盘赌法 选择selection |
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for i=(NoChangeNo+1):2:PopulationSize |
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[idx1,idx2] = selection(PopulationSize,FitnessSum,PopulationFitness,Index); |
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%% 父母交叉形成子代 |
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Rate=rand; |
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if Rate<=CrossRate |
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acr_position = floor(ChromosomeLen*rand+1); %交叉节点 |
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[PopulationNew1(i,:),PopulationNew1(i+1,:)]=crossover(acr_position,Population1(idx1,:),Population1(idx2,:)); |
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[PopulationNew2(i,:),PopulationNew2(i+1,:)]=crossover(acr_position,Population2(idx1,:),Population2(idx2,:)); |
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[PopulationNew3(i,:),PopulationNew3(i+1,:)]=crossover(acr_position,Population3(idx1,:),Population3(idx2,:)); |
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end |
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end |
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%% 变异 |
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for i=(NoChangeNo+1):PopulationSize |
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PopulationNew1(i,:)=mutation(ChromosomeLen,MutateRate,PopulationNew1(i,:)); |
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PopulationNew2(i,:)=mutation(ChromosomeLen,MutateRate,PopulationNew2(i,:)); |
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PopulationNew3(i,:)=mutation(ChromosomeLen,MutateRate,PopulationNew3(i,:)); |
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end |
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parfor i=1:PopulationSize |
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Population1(i,:)=PopulationNew1(i,:); |
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Population2(i,:)=PopulationNew2(i,:); |
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Population3(i,:)=PopulationNew3(i,:); |
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end |
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K_p(1,Iter)=Elite1(Iter,1); |
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K_i(1,Iter)=Elite2(Iter,1); |
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K_d(1,Iter)=Elite3(Iter,1); |
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end |
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figure(1) |
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plot(BestFitness,'LineWidth',2); |
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title('最有个体适应值','fontsize',18); |
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xlabel('迭代次数');ylabel('适应值'); |
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figure(2) |
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plot(K_p) |
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hold on |
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plot(K_i,'k','LineWidth',3) |
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plot(K_d,'--r') |
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title('pid参数优化曲线','fontsize',18); |
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