基于动态规划的混合遗传算法求解旅行商问题综述

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI:10.1109/SOCPAR.2013.7054102

Pham Dinh Thanh, Huynh Thi Thanh Binh, L. Bui

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引用次数: 14

摘要

旅行商问题(TSP)是一个著名的np困难问题。人们开发了许多算法来解决这一问题，并在合理的时间内给出了近乎最优的解。本文综述了遗传算法与动态规划相结合的TSP求解方法。针对这一问题，我们建立了遗传算法和差分算法的结合，并在7个由TSP-lib导出的欧几里得实例上进行了实验。实验结果表明，与遗传算法相比，实验算法的效率更高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A survey on hybridizing genetic algorithm with dynamic programming for solving the traveling salesman problem

Traveling Salesman Problem (TSP) is a well-known NP-hard problem. Many algorithms were developed to solve this problem and gave the nearly optimal solutions within reasonable time. This paper presents a survey about the combination Genetic Algorithm (GA) with Dynamic Programming (DP) for solving TSP. We also setup a combination between GA and DP for this problem and experimented on 7 Euclidean instances derived from TSP-lib. Experimental results are reported to show the efficiency of the experimented algorithm comparing to the genetic algorithm.

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2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)

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