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Create GA_PID.m

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TianZhendong 6 years ago
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      GA_PID.m

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GA_PID.m

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%%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')
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