From d9e91ab89b088f600987edaf0e6e66854c994f3a Mon Sep 17 00:00:00 2001
From: TianZhendong <33273211+TianZhendong@users.noreply.github.com>
Date: Thu, 11 Apr 2019 21:42:01 +0800
Subject: [PATCH] Delete GA_PID.m

---
 GA_PID.m | 180 -------------------------------------------------------
 1 file changed, 180 deletions(-)
 delete mode 100644 GA_PID.m

diff --git a/GA_PID.m b/GA_PID.m
deleted file mode 100644
index 3204ef5..0000000
--- a/GA_PID.m
+++ /dev/null
@@ -1,180 +0,0 @@
-%%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
-% CrossRate=0.6;                                      %交叉概率
-MutateRate=0.2;                                     %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
-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)]);         %显示迭代进度
-    %% 适应值计算Fitness
-    for i=1:PopulationSize
-        Kp=Population(i,1);
-        Ki=Population(i,2);
-        Kd=Population(i,3);
-        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(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 PopulationNew;
-    
-    %交叉操作  crossover
-%     for i=1:PopulationSize
-%         % rand<交叉概率，对两个个体的染色体串进行交叉操作
-%         if(rand < CrossRate)
-%             acr_position = floor(ChromosomeSize*rand+1);                %要交叉的节点
-% %             if (cross_position == 0 || cross_position == 1)
-% %                 continue;
-% %             end
-%             acr_chrom = floor((PopulationSize-1)*rand+1);                 %要交叉的染色体，floor取比它小的整数,acr_chrom取值在1-N    
-%             for j=1:acr_position
-%                 temp = Population(i,j);
-%                 Population(i,j) = Population(acr_chrom,j);
-%                 Population(acr_chrom,j) = temp;
-%             end
-%             
-%         end
-%     end
-%     clear i;
-% %     clear j;
-%     clear temp;
-%     clear acr_chrom;
-    
-    
-    
-    
-    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')
-
-
-
-
-
-