遗传算法matlab实现源程序Word文档下载推荐.docx
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ifobj(sequ)==objmin
opti=population(sequ,:
end
clearsequ;
fmax=22000;
%==
forgen=1:
maxgen
%选择操作
%将求最小值的函数转化为适应度函数
forindivi=1:
obj1(indivi)=1/obj(indivi);
clearindivi;
%适应度函数累加总合
total=0;
total=total+obj1(indivi);
%每条染色体被选中的几率
fitness1(indivi)=obj1(indivi)/total;
%各条染色体被选中的范围
fitness(indivi)=0;
forj=1:
indivi
fitness(indivi)=fitness(indivi)+fitness1(j);
clearj;
fitness;
%选择适应度高的个体
forranseti=1:
ran=rand;
while(ran>
1||ran<
0)
ran;
ifran<
=fitness
(1)
newpopulation(ranseti,:
)=population(1,:
else
forfet=2:
if(ran>
fitness(fet-1))&
&
(ran<
=fitness(fet))
)=population(fet,:
clearran;
newpopulation;
%交叉
forint=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)
ifcpoint1>
cpoint2
tem=cpoint1;
cpoint1=cpoint2;
cpoint2=tem;
cpoint1;
cpoint2;
forterm=cpoint1+1:
forss=1:
hromlength
ifpopcross(int,ss)==popfath(term)
tem1=popcross(int,ss);
popcross(int,ss)=popcross(int,term);
popcross(int,term)=tem1;
cleartem1;
ifpopcross(int+1,ss)==popmoth(term)
tem1=popcross(int+1,ss);
popcross(int+1,ss)=popcross(int+1,term);
popcross(int+1,term)=tem1;
cleartem1;
clearterm;
clearrandnum;
popcross;
%变异操作
newpop=popcross;
ifrandnum
cpoint12=round(rand*hromlength);
cpoint22=round(rand*hromlength);
if(cpoint12==0)
cpoint12=1;
if(cpoint22==0)
cpoint22=1;
while(cpoint22==cpoint12)
ifcpoint22==0;
temp=newpop(int,cpoint12);
newpop(int,cpoint12)=newpop(int,cpoint22);
newpop(int,cpoint22)=temp;
newpop;
clearcpoint12;
clearcpoint22;
clearint;
forium=1:
vector1=newpop(ium,:
obj1(ium)=hanshu(hromlength,vector1,phen);
clearium;
obj1max=max(obj1);
forar=1:
ifobj1(ar)==obj1max
newpop(ar,:
)=opti;
%遗传操作结束
二.粒子群算法源程序:
%------初始格式化--------------------------------------------------
clearall;
formatlong;
%------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)------------
fori=1:
N
D
x(i,j)=randn;
%随机初始化位置
v(i,j)=randn;
%随机初始化速度
%------先计算各个粒子的适应度,并初始化Pi和Pg----------------------
p(i)=fitness(x(i,:
),D);
y(i,:
)=x(i,:
pg=x(1,:
%Pg为全局最优
fori=2:
iffitness(x(i,:
),D)<
FITNESS(pg,D)
pg=x(i,:
%------进入主要循环,按照公式依次迭代,直到满足精度要求------------
fort=1:
MaxDT
t
fori=1:
v(i,:
)=w*v(i,:
)+c1*rand*(y(i,:
)-x(i,:
))+c2*rand*(pg-x(i,:
));
x(i,:
)+v(i,:
p(i)
ifp(i)<
pg=y(i,:
Pbest(t)=fitness(pg,D);
%------最后给出计算结果
disp('
*************************************************************'
)
函数的全局最优位置为:
'
Solution=pg'
disp