数学建模社区-数学中国

标题: 遗传算法 [打印本页]

作者: 遗传算法LN 时间: 2020-5-31 11:21
标题: 遗传算法
关于遗传算法的人员安排问题，NP问题，

作者: 遗传算法LN 时间: 2020-5-31 11:22
如何利用工具箱求解

作者: madio 时间: 2020-6-1 08:24
问题：
某公司指派n个员工到n个城市工作（每个城市单独一人），希望使所花费的总电话费用尽可能少。n个员工两两之间每个月通话的时间表示在下面的矩阵的上三角部分（因为通话的时间矩阵是对称的，没有必要写出下三角部分），n个城市两两之间通话费率表示在下面的矩阵的下三角部分（同样道理，因为通话的费率矩阵是对称的，没有必要写出上三角部分）. 试求解该二次指派问题。
0 5 3 7 9 3 9 2 9 0;
7 0 7 8 3 2 3 3 5 7;
4 8 0 9 3 5 3 3 9 3;
6 2 10 0 8 4 1 8 0 4;
8 6 4 6 0 8 8 7 5 9;
8 5 4 6 6 0 4 8 0 3;
8 6 7 9 4 3 0 7 9 5;
6 8 2 3 8 8 6 0 5 5;
6 3 6 2 8 3 7 8 0 5;
5 6 7 6 6 2 8 8 9 0;

答案：
工人2指派给城市1，工人7指派给城市2，工人4指派给城市3，工人9指派给城市4，工人8指派给城市5，工人5指派给城市6，工人6指派给城市7，工人3指派给城市8，工人1指派给城市9，工人10指派给城市10时，可取得最小费用每月1142.00元。
matlab源程序：
%遗传算法
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
M=[0 5 3 7 9 3 9 2 9 0;
7 0 7 8 3 2 3 3 5 7;
4 8 0 9 3 5 3 3 9 3;
6 2 10 0 8 4 1 8 0 4;
8 6 4 6 0 8 8 7 5 9;
8 5 4 6 6 0 4 8 0 3;
8 6 7 9 4 3 0 7 9 5;
6 8 2 3 8 8 6 0 5 5;
6 3 6 2 8 3 7 8 0 5;
5 6 7 6 6 2 8 8 9 0;];
M1=M;                %员工间每月通话时间矩阵
for i=1:10
for j=i+1:10
      M1(j,i)=M(i,j);
end
end
M2=M;             %两地间通话费率矩阵
for i=1:10
for j=i+1:10
      M2(i,j)=M(j,i);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%初始化种群
num=10;    %种群数量
code=10;    %染色体长
dai=100;       %遗传代数
inter=0.8;    %交叉率
byl=0.8;
%A=randperm(num*code);
for i=1:num
V(i,:)=randperm(10);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for gen=1:dai

%评估
[num1,lin]=size(V);
eval=zeros(num1,1);
for i=1:num1
      for j=1:code-1
         for k=j+1:code
            eval(i)=M1(V(i,j),V(i,k))*M2(j,k)+eval(i);
         end
      end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%选择
[eval1,ind]=sort(eval);
V1=V;
V=zeros(num,code);
for i=1:num
      V(i,:)=V1(ind(i),:);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%交叉
V1=V;
panduan=rand(fix(num),1);       %判断是否进行交叉
for i=1:fix(num);
      if panduan(i)<inter       %在交叉概率内进行交叉
         V2=zeros(1,code);       %记录交叉后的染色体
         h=randperm(num);             %随机取两个做交叉h(1)h(2)
         a=zeros(code,1);                %记录未使用的位置
         b=zeros(code,1);             %记录未使用的数字
         %在双亲中随机选择基因
         for i=1:code
            h2=randperm(2);             %在双亲中随机选择
            if b(V1(h(h2(1)),i))==0
                  V2(i)=V1(h(h2(1)),i); b(V1(h(h2(1)),i))=1; a(i)=1;
            end
         end

         %随机分配未使用数字和位置
         h1=randperm(code);             %记录未使用的数字
         for i=1:code
            for j=1:code
                  if b(i)==1&&h1(j)==i
                     h1(j)=0;break
                  end
            end
         end

         for i=1:code
            if V2(i)==0
                  for j=1:code
                     if h1(j)~=0
                        V2(i)=h1(j);h1(j)=0;break
                     end
                  end
            end
         end
         V=[V;V2];
      end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%变异
V1=V;
[num1,lin]=size(V);
x3=rand(num1,1);
for i=num1
      if x3(i)<byl             %变异率
         h2=randperm(code);
         V(i,h2(1))=V1(i,h2(2));
         V(i,h2(2))=V1(i,h2(1));
      end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%对最终种群进行评估
[num1,lin]=size(V);
eval=zeros(num1,1);
for i=1:num1
for j=1:code-1
      for k=j+1:code
         eval(i)=M1(V(i,j),V(i,k))*M2(j,k)+eval(i);
      end
end
end

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