Description: 用动态规划法求解旅行商问题 已经加入注释 欢迎批评指正-dynamic programming method for the traveling salesman problem has joined Notes welcome criticism correction Platform: |
Size: 422912 |
Author:卢林 |
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Description: 用动态规划算法编写的有关旅行商问题的java程序-dynamic programming algorithm used in the preparation of the Traveling Salesman Problem java programs Platform: |
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Author: |
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Description: 旅行商问题,即TSP问题(Travelling Salesman Problem)是数学领域中著名问题之一。假设有一个旅行商人要拜访n个城市,他必须选择所要走的路径,路经的限制是每个城市只能拜访一次,而且最后要回到原来出发的城市。路径的选择目标是要求得的路径路程为所有路径之中的最小值。
-Traveling Salesman Problem, or TSP problem (Travelling Salesman Problem) is a well-known field of mathematics problems. Assuming there is a travel business to visit n cities, he must choose which path to go, via the restriction is to visit each city only once and the last to go back to the original departure city. Path selection objective is to get the path distance for all paths are the minimum. Platform: |
Size: 1231872 |
Author:吴昆 |
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Description: tsp问题俗称旅行商问题,一个商人从一个城市出发,经过所有的城市一次且仅一次回到出发的城市,问旅行商应当如何选择路径使总路径最短。本程序是用lingo软件编写的,只需要更改城市的数目以及距离矩阵即可。-tsp problem commonly known as the traveling salesman problem, a businessman from a city of departure, after all of the city once and only once returned to the starting city TSP question should be how to choose the shortest path so that the total path. This procedure is used lingo software programmers only need to change the number of cities as well as the distance matrix can be. Platform: |
Size: 1024 |
Author:雷浩 |
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Description: 这是一个用遗传算法来求解旅行商问题(TSP问题:Travelling Salesman Problem)的源代码,学习遗传算法,演化计算的很值得一看!-This is a genetic algorithm to solve the traveling salesman problem (TSP problems: Travelling Salesman Problem) source code, learning genetic algorithm, evolutionary computation is very worth a visit! Platform: |
Size: 71680 |
Author:郭肇禄 |
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Description: TSP问题的动态规划求解。问题描述:旅行商问题,即TSP问题(Travelling Salesman Problem)是数学领域中著名问题之一。假设有一个旅行商人要拜访n个城市,他必须选择所要走的路径,路经的限制是每个城市只能拜访一次,而且最后要回到原来出发的城市。路径的选择目标是要求得的路径路程为所有路径之中的最小值。 -TSP dynamic programming to solve the problem. Description of the problem: traveling salesman problem, namely TSP problem (Travelling Salesman Problem) is a well-known field of mathematics problems. Assuming there is a travel businessmen to visit n cities, he must choose which path to go, via the restriction is to visit each city only once and the last to go back to the original departure city. Path selection objective is to get the path distance for all paths are the minimum. Platform: |
Size: 1045504 |
Author:黄艺敏 |
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Description: 用C语言实现旅行商问题。该算法简单精悍,效率也不低,是实现TSP问题的很好的算法。-Using C language realize traveling salesman problem. The algorithm is simple and lean, efficiency is not low, the problem is to achieve a good TSP algorithms. Platform: |
Size: 236544 |
Author:锭 |
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Description: 这是一个货郎担问题的动态规划代码,是C语言版的,能在TC上和VC++6.0上运行.加入了许多注释.-This is a traveling salesman problem dynamic programming code, is a C language version, can TC and VC++ 6.0 run. Adding a lot of notes. Platform: |
Size: 2048 |
Author:zhoulitc |
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Description: 用A星算法解决旅行商(货郎担)问题,附设计报告和测试用例-A Star algorithm used to solve TSP (traveling salesman) problems, with the design of reports and test cases Platform: |
Size: 60416 |
Author:杨猛 |
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Description: This program gives solution to the Travelling Salesman problem (TSP). I use genetic algorithm, tabu search, exhaustive search. It all combine in a windows application. Platform: |
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Author:yyavuz |
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Description: 旅行商问题(Travelling Salesman Problem, 简记TSP,亦称货郎担问题):设有n个城市和距离矩阵D=[dij],其中dij表示城市i到城市j的距离,i,j=1,2 … n,则问题是要找出遍访每个城市恰好一次的一条回路并使其路径长度为最短。-TSP (Travelling Salesman Problem, Jane Hutchison TSP, also known as the traveling salesman problem): with n cities and the distance matrix D = [dij], which said dij city i to city j the distance, i, j = 1, 2 ... n, the problem is to identify each city exactly once visited a loop and make it to the shortest path length. Platform: |
Size: 268288 |
Author:章为到 |
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Description: 一篇关于模拟退火算法解决TSP问题的MATLAB代码,拿来分享,欢迎讨论-On a simulated annealing algorithm to solve TSP problem of MATLAB code, used to share, please discuss Platform: |
Size: 9216 |
Author:Allen |
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