改进储蓄算法生成的旅行商路径问题的遗传算法

M. A. Bisma, Ekra Sanggala
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引用次数: 0

摘要

旅行推销员问题(TSP)是一个从起始节点出发,经过恰好访问一次节点数量,最终返回起始节点的最短路径问题。如果一个TSP有很多节点,它将是一个np困难问题。基于启发式和元启发式的算法是求解np困难问题的一种解决方案。储蓄算法是一种启发式算法,所以它的解可能不是最好的解,因此存在改进的机会。遗传算法是一种元启发式算法,可以应用于许多优化问题,包括TSP问题。本文将讨论遗传算法对节省算法生成的路由TSP的改进。通过对10个实例的测试,表明基于遗传算法的算法可以改进由节省算法生成的TSP路由。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Algorithm for Improving Route of Travelling Salesman Problem Generated by Savings Algorithm
Travelling Salesman Problem (TSP) is the problem for finding the shortest route starting from start node then visiting number of nodes exactly once and finally go back to start node. If a TSP has a lot of nodes, it will be a NP-Hard Problem. Algorithms working based on heuristic and metaheuristic can be a solution for solving NP-Hard Problem. Savings Algorithm is a heuristic algorithm, so it’s solution may be not the best solution, therefore there is a chance to improve it. Genetic Algorithm is a metaheuristic that can be applied on many optimization problems, including TSP. This paper will discuss about GA for improving route TSP generated by Savings Algorithm. On testing of 10 instances, showing that algorithm based on GA can improve route of TSP generated by Savings Algorithm.
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