蚁群优化(ACO)与禁忌搜索(TS)算法结合求解旅行商问题

Rico Wijaya Dewantoro, P. Sihombing, Sutarman
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引用次数: 20

摘要

本文提出将蚁群优化算法与禁忌搜索算法相结合作为局部搜索来解决旅行商问题。这是一种混合蚁群算法,可以找到最佳路线并获得更好的运行时间。可以使用的经典问题之一是TSP。在本研究中,作者将比较ACO- ts和ACO的杂交。在本研究中,蚁群算法和蚁群算法的混合算法比蚁群算法获得了最佳路径和更好的运行时间。说明ACO- ts联合使用优于ACO本身。因此,为了获得最佳路径和较好的运行时间,作者提出了ACO-TS算法来求解TSP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Combination of Ant Colony Optimization (ACO) and Tabu Search (TS) Algorithm to Solve the Traveling Salesman Problem (TSP)
In this research, the authors want to propose the combination of Ant Colony Optimization Algorithm and Tabu Search Algorithm as local search to solve Traveling Salesman Problem. This is a hybrid method of ACO to find best routes and get a better running time. One of the classic problems that can be used is TSP. In this research, the authors will compare the hybrid of ACO-TS and ACO. In this research, the hybrid of ACO-TS got the best routes and a better running time than ACO itself. It means that combination of ACO-TS is better than ACO itself. Therefore to get the best routes and a better running time, the author suggested the ACO-TS algorithm to solve TSP.
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