通信网络链路容量和流量优化遗传算法MATLAB源码
function =GACFA(M,N,Pm)%--------------------------------------------------------------------------
%GACFA.m
%Genetic Algorithm for Capacity and Flow Assignment
%链路容量和流量优化分配的遗传算法
%GreenSim团队原创作品，转载请注明
%--------------------------------------------------------------------------
%函数功能
%使用遗传算法求解通信网链路容量和流量联合优化分配问题
%--------------------------------------------------------------------------
%参考文献
%叶大振,吴新余.基于遗传算法的计算机通信网优化设计.
%南京邮电学院学报.1996,16(2):9-15
%--------------------------------------------------------------------------
%输入参数列表
%M 遗传进化迭代次数
%N 种群规模(取偶数)
%Pm 变异概率
%--------------------------------------------------------------------------
%输出参数列表
%Zp 目标函数最优值
%Xp 路由选择决策变量最优值
%Yp 线路型号决策变量最优值
%LC1 收敛曲线1，各代最优个体适应值的记录
%LC2 收敛曲线2，各代群体平均适应值的记录
%--------------------------------------------------------------------------
%第一步：载入数据和输出变量初始化
load DATA_CFA;
Xp=zeros(14,1);
Yp=zeros(8,3);
LC1=zeros(1,M);
LC2=LC1;
%第二步：随机产生初始种群
farm_X=zeros(14,N);
farm_Y=zeros(8,3*N);
for i=1:N
for j=1:2:13
RAND=rand;
if RAND>0.5
farm_X(j,i)=1;
else
farm_X(j+1,i)=1;
end
end
end
for i=1:N
for j=1:8
RAND=rand;
if RAND<1/3
farm_Y(j,3*i-2)=1;
elseif RAND>2/3
farm_Y(j,3*i)=1;
else
farm_Y(j,3*i-1)=1;
end
end
end
counter=0;%设置迭代计数器
while counter<M%停止条件为达到最大迭代次数
%第三步：交叉
newfarm_X=zeros(14,N);
newfarm_Y=zeros(8,3*N);
Ser=randperm(N);
%对X做交叉
for i=1:2:(N-1)
A_X=farm_X(:,Ser(i));
B_X=farm_X(:,Ser(i+1));
cp=2*unidrnd(6);
a_X=;
b_X=;
newfarm_X(:,i)=a_X;
newfarm_X(:,i+1)=b_X;
end
%对Y做交叉
for i=1:2:(N-1)
A_Y=farm_Y(:,(3*Ser(i)-2):(3*Ser(i)));
B_Y=farm_Y(:,(3*Ser(i+1)-2):(3*Ser(i+1)));
cp=unidrnd(7);
a_Y=;
b_Y=;
newfarm_Y(:,(3*i-2):(3*i))=a_Y;
newfarm_Y(:,(3*i+1):(3*i+3))=b_Y;
end
%新旧种群合并
FARM_X=;
FARM_Y=;
%第四步：选择复制
Ser=randperm(2*N);
FITNESS=zeros(1,2*N);
fitness=zeros(1,N);
for i=1:(2*N)
X=FARM_X(:,i);
Y=FARM_Y(:,(3*i-2):(3*i));
FITNESS(i)=COST(X,Y,x1_x14,F_x1_x14,A,Q,C,S,b);
end
for i=1:N
f1=FITNESS(Ser(2*i-1));
f2=FITNESS(Ser(2*i));
if f1<f2
farm_X(:,i)=FARM_X(:,Ser(2*i-1));
farm_Y(:,(3*i-2):(3*i))=FARM_Y(:,(3*Ser(2*i-1)-2):(3*Ser(2*i-1)));
fitness(i)=f1;
else
farm_X(:,i)=FARM_X(:,Ser(2*i));
farm_Y(:,(3*i-2):(3*i))=FARM_Y(:,(3*Ser(2*i)-2):(3*Ser(2*i)));
fitness(i)=f2;
end
end
%记录最佳个体和收敛曲线
minfitness=min(fitness);
meanfitness=mean(fitness);
LC1(counter+1)=minfitness;
LC2(counter+1)=meanfitness;
pos=find(fitness==minfitness);
Xp=farm_X(:,pos(1));
Yp=farm_Y(:,(3*pos(1)-2):(3*pos(1)));
Zp=minfitness;
%第五步：变异
for i=1:N
if Pm>rand
GT_X=farm_X(:,i);
GT_Y=farm_Y(:,(3*i-2):(3*i));
pos1=2*unidrnd(7);
if GT_X(pos1)==1
GT_X(pos1-1)=1;
GT_X(pos1)=0;
farm_X(:,i)=GT_X;
elseif GT_X(pos1)==0
GT_X(pos1-1)=0;
GT_X(pos1)=1;
farm_X(:,i)=GT_X;
else
end
pos2=unidrnd(8);
GT_Y(pos2,:)=zeros(1,3);
GT_Y(pos2,unidrnd(3))=1;
end
end
counter=counter+1
end
Xp=Xp';
Yp=Yp';
%plot(LC1)
%hold on
plot(LC2) 谢谢楼主分享！学习学习！！！！！
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