TianZhendong
6 years ago
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GitHub
1 changed files with 148 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 = 3; %染色体长度，参数的个数，PID有三个参数 |
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PopulationSize = 100; %种群规模 |
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MaxIter = 100; %最大迭代次数 |
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% MinFitness=0.01; %最小适应值 这里PID适应度函数取的倒数，因此应为最大。 |
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% CrossRate=0.6; %交叉概率 |
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MutateRate=0.2; %变异概率 |
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NoChangeNo=5; %直接复制的父辈的染色体数量 |
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UpLimit=30; %基因上限 |
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%% 全局变量 pid的三个参数 |
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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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Population=rand(PopulationSize,ChromosomeSize); %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:ChromosomeSize |
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Population(i,j)=rand*30; %产生0-30之间的随机数用来初始化种群，30表示每个值不超过30 |
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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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%Iter =1; |
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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); |
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Elite=zeros(MaxIter,ChromosomeSize); %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)]); |
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% 适应值计算Fitness |
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for i=1:PopulationSize |
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Kp=Population(i,1); |
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Ki=Population(i,2); |
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Kd=Population(i,3); |
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PopulationFitness(i,:) = PSO_PID(Kp,Ki,Kd); |
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end |
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% 适应值大小排序，并保存最佳 |
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FitnessSum=sum(PopulationFitness); %种群累加适应度 |
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AveFitness(Iter,1)=FitnessSum/PopulationSize; |
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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(MaxIter,1); %最佳适应度 |
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Elite(Iter,:) = Population(Index(MaxIter),:); %记录本代的精英 |
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% if(BestFitness(Iter,1)<MinFitness) %判断是否达到要求的适应值 |
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% break; |
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% end |
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% FitnessCumsum=cumsum(PopulationFitness); %累加适应度数组 |
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%复制适应值最大的不变的种群 |
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PopulationNew=zeros(PopulationSize,ChromosomeSize); |
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for i=1:NoChangeNo |
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PopulationNew(i,:)=Population(Index(MaxIter-i+1),:); |
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end |
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%轮盘赌法 选择selection要交叉的父母 |
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for i=(NoChangeNo+1):2:PopulationSize |
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%确定要交叉的父亲染色体序号 |
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idx1=0; |
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idx2=0; |
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m1=0; |
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r1=rand*FitnessSum; |
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for k=1:PopulationSize |
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m1=m1+PopulationFitness(k); |
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if r1<m1 |
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idx1=Index(k); |
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break; |
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end |
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end |
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%确定要交叉的母亲染色体序号 |
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m2=0; |
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r2=rand*FitnessSum; |
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for k=1:PopulationSize |
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m2=m2+PopulationFitness(k); |
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if r2<m2 |
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idx2=Index(k); |
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break; |
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end |
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end |
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acr_position = floor(ChromosomeSize*rand+1); %要交叉的节点 |
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%交叉 |
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for j=1:acr_position |
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temp = Population(idx1,j); |
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Population(idx1,j) = Population(idx2,j); |
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Population(idx2,j) = temp; |
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end |
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%将产生的两个子代添加到新的种群中 |
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PopulationNew(i,:)=Population(idx1,:); |
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PopulationNew(i+1,:)=Population(idx2,:); |
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end |
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%变异 mutation |
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for i=(NoChangeNo+1):PopulationSize |
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for j=1:ChromosomeSize |
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mut_rand = rand; %是否变异 |
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if mut_rand < MutateRate |
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mut_pm = rand; %增加还是减少 |
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mut_num = rand*(1-AveFitness(Iter)/BestFitness(Iter))^2; |
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if PopulationNew(i, j)>=UpLimit |
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PopulationNew(i, j)= PopulationNew(i, j)*(1-mut_num); |
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elseif mut_pm<=0.5 |
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PopulationNew(i, j)= PopulationNew(i, j)*(1-mut_num); |
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else |
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PopulationNew(i, j)= PopulationNew(i, j)*(1+mut_num); |
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end |
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if PopulationNew(i, j)>=UpLimit |
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PopulationNew(i, j)=UpLimit; |
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end |
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end |
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end |
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end |
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parfor i=1:PopulationSize |
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for j=1:ChromosomeSize |
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Population(i,j)=PopulationNew(i,j); |
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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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clear first; |
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clear last; |
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clear idx; |
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clear mid; |
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clear i; |
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clear j; |
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K_p(1,Iter)=Elite(Iter,1); |
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K_i(1,Iter)=Elite(Iter,2); |
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K_d(1,Iter)=Elite(Iter,3); |
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end |
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figure(1) |
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plot(BestFitness,'LineWidth',2); |
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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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