tsp.cpp

ga算法解tsp问题.动态TSP就是城市坐标在随着时间变化,我们的目标则要在最短的时间窗内寻找出最优的城市遍历路径,这是个双最优问题. 这是我对动态TSP算法的理解,使用改进的反序-杂交算法

#include <iostream>
#include <vector>    
#include <algorithm>
#include <conio.h>
#include <time.h>
#include <stdlib.h>
#include "def.h"
#include "TSP.h"



void main()
{
     
     const static DISTANCE distance[][_CITY_AMOUNT] =
	 {
	  {0, 1, 4, 6, 8, 1, 3, 7, 2, 9, 7, 3, 4, 5, 8, 9, 2, 8, 2, 8},{1, 0, 7, 5, 3, 8, 3, 4, 2, 4, 4, 6, 2, 8, 2, 9, 4, 5, 2, 1},{4, 7, 0, 3, 8, 3, 7, 9, 1, 2, 5, 8, 1, 8, 9, 4, 7, 4, 8, 4},{6, 5, 3, 0, 3, 1, 5, 2, 9, 1, 3, 5, 7, 3, 4, 7, 3, 4, 5, 2},
{8, 3, 8, 3, 0, 2, 3, 1, 4, 6, 3, 8, 4, 5, 2, 8, 1, 7, 4, 7},{1, 8, 3, 1, 2, 0, 3, 3, 9, 5, 4, 5, 2, 7, 3, 6, 2, 3, 7, 1},{3, 3, 7, 5, 3, 3, 0, 7, 5, 9, 3, 4, 5, 9, 3, 7, 3, 2, 8, 1},{7, 4, 9, 2, 1, 3, 7, 0, 1, 3, 4, 5, 2, 7, 6, 3, 3, 8, 3, 5},
{2, 2, 1, 9, 4, 9, 5, 1, 0, 1, 3, 4, 7, 3, 7, 5, 9, 2, 1, 7},{9, 4, 2, 1, 6, 5, 9, 3, 1, 0, 3, 7, 3, 7, 4, 9, 3, 5, 2, 5},{7, 4, 5, 3, 3, 4, 3, 4, 3, 3, 0, 5, 7, 8, 4, 3, 1, 5, 9, 3},{3, 6, 8, 5, 8, 5, 4, 5, 4, 7, 5, 0, 8, 3, 1, 5, 8, 5, 8, 3},
{4, 2, 1, 7, 4, 2, 5, 2, 7, 3, 7, 8, 0, 5, 7, 4, 8, 3, 5, 3},{5, 8, 8, 3, 5, 7, 9, 7, 3, 7, 8, 3, 5, 0, 8, 3, 1, 8, 4, 5},{8, 2, 9, 4, 2, 3, 3, 6, 7, 4, 4, 1, 7, 8, 0, 4, 2, 1, 8, 4},{9, 9, 4, 7, 8, 6, 7, 3, 5, 9, 3, 5, 4, 3, 4, 0, 4, 1, 8, 4},
{2, 4, 7, 3, 1, 2, 3, 3, 9, 3, 1, 8, 8, 1, 2, 4, 0, 4, 3, 7},{8, 5, 4, 4, 7, 3, 2, 8, 2, 5, 5, 5, 3, 8, 1, 1, 4, 0, 2, 6},{2, 2, 8, 5, 4, 7, 8, 3, 1, 2, 9, 8, 5, 4, 8, 8, 3, 2, 0, 4},{8, 1, 4, 2, 7, 1, 1, 5, 7, 5, 3, 3, 3, 5, 4, 4, 7, 6, 4, 0}
	 };                          
	 //BROKEN代表两个城市之间没有直接连通                                
	 //城市间的距离矩阵
      //distance[i][j]代表i城市与j城市的距离

     Csga<_CONTAINER, _CONTAINER_P> CUnit((DISTANCE *)distance);     //初始化
     //开始遗传算法
     if(!CUnit.fnCreateRandomGene())     //产生随机的基因
     {
         exit(0);
     }
    
     //循环基因编译,杂交,淘汰过程
     CUnit.fnEvalAll();
     for ( int i = 0; i < _TIMES; ++i )
     {
         //CUnit.fnDispProbability();
         CUnit.fnGeneAberrance();       //基因变异
         //CUnit.fnDispProbability();
         CUnit.fnGeneMix();            //基因杂交
         CUnit.fnEvalAll();                                         
         
         //每隔_DISP_INTERVAL显示一次结果
         if ( (i+1)%_DISP_INTERVAL == 0 || i == 0)
         {
             cout << "第" << i+1 << "代" << std::endl;
             CUnit.fnDispProbability();
             CUnit.fnDispHistoryMin();
         }
     
     }

     CUnit.fnDispHistoryMin();
}