Create GA_PID.m

main code

GA_PID.m

@@ -0,0 +1,148 @@
+%%清空环境
+clc;
+clear;
+close all;
+
+%%参数设置
+ChromosomeSize = 3;                                 %染色体长度，参数的个数，PID有三个参数
+PopulationSize = 100;                               %种群规模
+MaxIter = 100;                                      %最大迭代次数
+% 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。
+% CrossRate=0.6;                                      %交叉概率
+MutateRate=0.2;                                     %变异概率
+NoChangeNo=5;                                       %直接复制的父辈的染色体数量       
+UpLimit=30;                                              %基因上限
+%% 全局变量 pid的三个参数
+global Kp;
+global Ki;
+global Kd;
+
+%% 初始化种群init.m
+Population=rand(PopulationSize,ChromosomeSize);    %种群,预分配内存
+for i=1:PopulationSize
+    disp(['正在初始化种群',num2str(i)]);
+    for j=1:ChromosomeSize
+        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
+    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);
+Elite=zeros(MaxIter,ChromosomeSize);                  %用于记录每一代的最优解
+
+for Iter=1:MaxIter
+    disp(['迭代次数：',num2str(Iter)]);
+    % 适应值计算Fitness
+    for i=1:PopulationSize
+        Kp=Population(i,1);
+        Ki=Population(i,2);
+        Kd=Population(i,3);
+        PopulationFitness(i,:) = PSO_PID(Kp,Ki,Kd);
+    end
+
+    % 适应值大小排序，并保存最佳
+    FitnessSum=sum(PopulationFitness);                 %种群累加适应度
+    AveFitness(Iter,1)=FitnessSum/PopulationSize;
+    [PopulationFitness,Index]=sort(PopulationFitness);         %适应值从小到大排序
+    BestFitness(Iter,1) = PopulationFitness(MaxIter,1);                %最佳适应度
+    Elite(Iter,:) = Population(Index(MaxIter),:);                    %记录本代的精英
+%     if(BestFitness(Iter,1)<MinFitness)                        %判断是否达到要求的适应值
+%         break;
+%     end
+    
+%     FitnessCumsum=cumsum(PopulationFitness);                   %累加适应度数组
+    
+    
+   %复制适应值最大的不变的种群
+    PopulationNew=zeros(PopulationSize,ChromosomeSize);
+    for i=1:NoChangeNo
+        PopulationNew(i,:)=Population(Index(MaxIter-i+1),:);
+    end
+    
+    %轮盘赌法   选择selection要交叉的父母    
+    for i=(NoChangeNo+1):2:PopulationSize
+        %确定要交叉的父亲染色体序号
+        idx1=0;
+        idx2=0;
+        m1=0;
+        r1=rand*FitnessSum;
+        for k=1:PopulationSize
+            m1=m1+PopulationFitness(k);
+            if r1<m1
+                idx1=Index(k);
+                break;
+            end
+        end
+         %确定要交叉的母亲染色体序号
+        m2=0;
+        r2=rand*FitnessSum;
+        for k=1:PopulationSize
+            m2=m2+PopulationFitness(k);
+            if r2<m2
+                idx2=Index(k);
+                break;
+            end
+        end
+        acr_position = floor(ChromosomeSize*rand+1);                %要交叉的节点
+        %交叉
+        for j=1:acr_position
+            temp = Population(idx1,j);
+            Population(idx1,j) = Population(idx2,j);
+            Population(idx2,j) = temp;
+        end
+        %将产生的两个子代添加到新的种群中
+        PopulationNew(i,:)=Population(idx1,:);
+        PopulationNew(i+1,:)=Population(idx2,:);
+    end
+    
+    %变异  mutation
+    for i=(NoChangeNo+1):PopulationSize
+        for j=1:ChromosomeSize
+            mut_rand = rand; %是否变异
+            if mut_rand < MutateRate
+                mut_pm = rand; %增加还是减少
+                mut_num = rand*(1-AveFitness(Iter)/BestFitness(Iter))^2;
+                if PopulationNew(i, j)>=UpLimit
+                    PopulationNew(i, j)= PopulationNew(i, j)*(1-mut_num);
+                elseif mut_pm<=0.5
+                    PopulationNew(i, j)= PopulationNew(i, j)*(1-mut_num);
+                else
+                    PopulationNew(i, j)= PopulationNew(i, j)*(1+mut_num);
+                end
+                if PopulationNew(i, j)>=UpLimit
+                    PopulationNew(i, j)=UpLimit;
+                end
+            end
+        end
+    end
+	parfor i=1:PopulationSize
+        for j=1:ChromosomeSize
+            Population(i,j)=PopulationNew(i,j);
+        end
+	end
+    clear i;
+    clear j;
+    clear first;
+    clear last;
+    clear idx;
+    clear mid;
+    clear i;
+    clear j;
+    
+    K_p(1,Iter)=Elite(Iter,1);
+    K_i(1,Iter)=Elite(Iter,2);
+    K_d(1,Iter)=Elite(Iter,3);
+end
+figure(1)
+plot(BestFitness,'LineWidth',2);
+
+figure(2)
+plot(K_p)
+hold on
+plot(K_i,'k','LineWidth',3)
+plot(K_d,'--r')