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126 lines
5.6 KiB
126 lines
5.6 KiB
%%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 = 100; %种群规模
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MaxIter = 50; %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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for 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(Iter,1)=Elite1(Iter,1);
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K_i(Iter,1)=Elite2(Iter,1);
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K_d(Iter,1)=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('最优个体适应值','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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