遗传算法matlab实现源程序.doc

附页：
一．遗传算法源程序：
clc;
clear;
population;
%评价目标函数值
for uim=1:popsize
    vector=population(uim,:);
    obj(uim)=hanshu(hromlength,vector,phen);
end
%obj
%min(obj)
clear uim;
objmin=min(obj);
for sequ=1:popsize
    if obj(sequ)==objmin
        opti=population(sequ,:);
    end
end
clear sequ;
fmax=22000;
%==
for gen=1:maxgen
%选择操作
%将求最小值的函数转化为适应度函数
for indivi=1:popsize
    obj1(indivi)=1/obj(indivi);
end
clear indivi;
%适应度函数累加总合
total=0;
for indivi=1:popsize
    total=total+obj1(indivi);
end
clear indivi;
%每条染色体被选中的几率
for indivi=1:popsize
    fitness1(indivi)=obj1(indivi)/total;
end
clear indivi;
%各条染色体被选中的范围
for indivi=1:popsize
    fitness(indivi)=0;
    for j=1:indivi
        fitness(indivi)=fitness(indivi)+fitness1(j);
    end
end
clear j;
fitness;
%选择适应度高的个体
for ranseti=1:popsize
    ran=rand;
    while (ran>1||ran<0)
        ran=rand;
    end
    ran;
    if ran<=fitness(1)
        newpopulation(ranseti,:)=population(1,:);
    else
        for fet=2:popsize
            if (ran>fitness(fet-1))&&(ran<=fitness(fet))
                newpopulation(ranseti,:)=population(fet,:);
            end
        end
    end
end
clear ran;
newpopulation;
%交叉
for int=1:2:popsize-1
    popmoth=newpopulation(int,:);                     
    popfath=newpopulation(int+1,:);                 
    popcross(int,:)=popmoth;
    popcross(int+1,:)=popfath;
    randnum=rand;
    if(randnum< P>
        cpoint1=round(rand*hromlength);         
        cpoint2=round(rand*hromlength);           
        while (cpoint2==cpoint1)                 
            cpoint2=round(rand*hromlength);
        end
        if cpoint1>cpoint2                     
            tem=cpoint1;
            cpoint1=cpoint2;
            cpoint2=tem;
        end
        cpoint1;
        cpoint2;
        for term=cpoint1+1:cpoint2                   
            for ss=1:hromlength
                if popcross(int,ss)==popfath(term)
                    tem1=popcross(int,ss);
                    popcross(int,ss)=popcross(int,term);
                    popcross(int,term)=tem1;
                end
            end
            clear tem1;
        end
        for term=cpoint1+1:cpoint2                   
            for ss=1:hromlength
                if popcross(int+1,ss)==popmoth(term)
                    tem1=popcross(int+1,ss);
                    popcross(int+1,ss)=popcross(int+1,term);
                    popcross(int+1,term)=tem1;
                end
            end
            clear tem1;
        end
    end
    clear term;
end
clear randnum;
popcross;
%变异操作
newpop=popcross;
for int=1:popsize
    randnum=rand;
    if randnum
        cpoint12=round(rand*hromlength);         
        cpoint22=round(rand*hromlength);        
        if (cpoint12==0)
            cpoint12=1;
        end
        if (cpoint22==0)
            cpoint22=1;
        end
        while (cpoint22==cpoint12)                
            cpoint22=round(rand*hromlength);
            if cpoint22==0;
                cpoint22=1;
            end
        end
        temp=newpop(int,cpoint12);
        newpop(int,cpoint12)=newpop(int,cpoint22);
        newpop(int,cpoint22)=temp;
    end
end
newpop;
clear cpoint12;
clear cpoint22;
clear randnum;
clear int;
for ium=1:popsize
    vector1=newpop(ium,:);
    obj1(ium)=hanshu(hromlength,vector1,phen);
end
clear ium;
obj1max=max(obj1);
for ar=1:popsize
    if obj1(ar)==obj1max
        newpop(ar,:)=opti;
    end
end
%遗传操作结束
二．粒子群算法源程序：
%------初始格式化--------------------------------------------------
clear all;
clc;
format long;
%------给定初始化条件----------------------------------------------
c1=1.4962;%学习因子1
c2=1.4962;%学习因子2
w=0.7298;%惯性权重
MaxDT=100;%最大迭代次数
D=2;%搜索空间维数（未知数个数）
N=40;%初始化群体个体数目
eps=10^(-6);%设置精度(在已知最小值时候用)
%------初始化种群的个体(可以在这里限定位置和速度的范围)------------
for i=1:N
for j=1:D
x(i,j)=randn;%随机初始化位置
v(i,j)=randn;%随机初始化速度
end
end
%------先计算各个粒子的适应度，并初始化Pi和Pg----------------------
for i=1:N
p(i)=fitness(x(i,:),D);
y(i,:)=x(i,:);
end
pg=x(1,:);%Pg为全局最优
for i=2:N
if fitness(x(i,:),D)<FITNESS(pg,D)
pg=x(i,:);
end
end
%------进入主要循环，按照公式依次迭代，直到满足精度要求------------
for t=1:MaxDT
t
for i=1:N
v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg-x(i,:));
x(i,:)=x(i,:)+v(i,:);
if fitness(x(i,:),D)<p(i)
p(i)=fitness(x(i,:),D);
y(i,:)=x(i,:);
end
if p(i)<FITNESS(pg,D)
pg=y(i,:);
end
end
Pbest(t)=fitness(pg,D);
end
%------进入主要循环，按照公式依次迭代，直到满足精度要求------------
for t=1:MaxDT
for i=1:N
v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg-x(i,:));
x(i,:)=x(i,:)+v(i,:);
if fitness(x(i,:),D)<p(i)
p(i)=fitness(x(i,:),D);
y(i,:)=x(i,:);
end
if p(i)<FITNESS(pg,D)
pg=y(i,:);
end
end
Pbest(t)=fitness(pg,D);
end
%------最后给出计算结果
disp('*************************************************************')
disp('函数的全局最优位置为：')
Solution=pg'
disp('最后得到的优化极值为：')
Result=fitness(pg,D)
disp('*************************************************************')
[X,Y]=meshgrid(-500:2:500);
Z=X.*sin(sqrt(X))+Y.*(sin(sqrt(Y)));
hold on
contour(X,Y,Z)
plot(x(:,1),x(:,2),'*');
hold off