function result=sga(n,a,b,pc,pm,e)
%n—群体规模；a—搜索上限；b—搜索下限；
%pc—交叉概率；pm—变异概率；e—计算精度；
q=[100 100 100 100 100 300 300 300 300 300 500 500 500 500 500 700 700 700 700 700 900 900 900 900 900];
w=[100 300 500 700 900 100 300 500 700 900 100 300 500 700 900 100 300 500 700 900 100 300 500 700 900];
o=[0.006 0.005 0.006 0.007 0.007 0.005 0.007 0.007 0.008 0.006 0.006 0.007 0.008 0.008 0.009 0.007 0.007 0.007 0.008 0.009 0.006 0.008 0.009 0.009 0.010];
A=[q',w',ones(size(q'))];
B=o';
abc=A\B;
r1=abc(1);
r2=abc(2);
r3=abc(3);
x=(o-r1.*q-r2.*w-r3);
for i=1:50 %求出群体的码串最小长度m
if (b-a)/e>2^(i)
m=i+1;
else
i=i+1;
end
end
popusize=n;chromlength=m;j=1;
popu=round(rand(popusize,chromlength)); %随机产生n行m列的初始群体
while j<=30 %设置程序中止条件
py=chromlength;
for i=1:py %进行二进制转换成十进制的解码操作
popu1(:,i)=2.^(py-1).*popu(:,i);
py=py-1;
end
popu2=sum(popu1,2);
x=a+popu2*(b-a)/(2^i-1);
yvalue=x;%计算群体中每个个体的适应度
for i=1:popusize %执行复制操作
if yvalue(i)<0
yvalue(i)=0;
end
end
fitscore=yvalue/sum(yvalue);%个体被选中的概率
fitscore=cumsum(fitscore);% 群体中个体的累积概率
wh=sort(rand(popusize,1));% 从小到大排列
wheel=1;fitone=1;
while wheel<=popusize %执行转盘式选择操作
if wh(wheel)<fitscore(fitone)
newpopu(wheel,:)=popu(fitone,:);
wheel=wheel+1;
else
fitone=fitone+1;
end
end
popu=newpopu;
for i=1:2:popusize-1 %执行交叉操作
if rand<pc
cpoint=round(rand*chromlength);
newpopu(i,:)=[popu(i,1:cpoint) popu(i+1,cpoint+1:chromlength)];
newpopu(i+1,:)=[popu(i+1,1:cpoint) popu(i,cpoint+1:chromlength)];
else
newpopu(i,:)=popu(i,:);
newpopu(i+1,:)=popu(i+1,:);
end
end
popu=newpopu;
for i=1:popusize %执行变异操作
if rand<pm
mpoint=round(rand*chromlength);
if mpoint<=0;
mpoint=1;
end
newpopu(i,:)=popu(i,:);
if newpopu(i,mpoint)==0
newpopu(i,mpoint)=1;
else
newpopu(i,mpoint)=0;
end
else
newpopu(i,:)=popu(i,:);
end
end
[y(j) index]=max(yvalue); %求出群体中适应值最大的个体及其适应值
bestindividual=newpopu(index,:);
py=chromlength;
for i=1:py %进行二进制转换成十进制的解码操作
bestindividual(1,i)=2.^(py-1).*bestindividual(:,i);
py=py-1;
end
r(j)=a+sum(bestindividual,2)*(b-a)/(2^i-1);
popu=newpopu;j=j+1;% 重新赋值并返回
end
fplot('x',[a b]);
hold on;plot(r,y,'r*');hold off
xlabel('x');ylabel('y')
[y index]=max(y); %计算最大值及其位置
result=[r(index) y];% 返回优化结果
end
