Create main.m

GA_PID/main.m

@@ -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
+global Kp;
+global Ki;
+global Kd;
+%% 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
+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
+K_p=zeros(MaxIter,1);                        %初始化 用于提高运算速度
+K_i=zeros(MaxIter,1);                        %初始化 用于提高运算速度
+K_d=zeros(MaxIter,1);                        %初始化 用于提高运算速度
+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,:)));
+        Kp=decode(Population1(i,:));
+        Ki=decode(Population2(i,:));
+        Kd=decode(Population3(i,:));
+        PopulationFitness(i,1) = fitness(Kp,Ki,Kd);
+    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);
+title('最有个体适应值','fontsize',18);
+xlabel('迭代次数');ylabel('适应值');
+figure(2)
+plot(K_p)
+hold on
+plot(K_i,'k','LineWidth',3)
+plot(K_d,'--r')
+title('pid参数优化曲线','fontsize',18);