Variation of ant colony optimization parameters for solving the travelling salesman problem

Pui Yue Cheong, Deepak Aggarwal, T. Hanne, Rolf Dornberger
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引用次数: 11

Abstract

This paper describes the Ant Colony Optimization (ACO) algorithm for solving the Travelling Salesman Problem. ACO is a swarm intelligence approach where the agents (ants) communicate using a chemical substance called pheromone, which evaporates over time. This principle is used for finding the shortest possible route between cities based on previously investigated connections. The algorithm is evaluated to get results for a different number of cities corresponding to small, medium and, large problem instances. Accordingly, the problem size is varied to compare different results with the change in size of the ant colony and other parameters. The ant colony algorithm is also compared with other algorithms such as the Kohonen and the Christofides heuristic algorithms.
求解旅行商问题的蚁群优化参数变化
本文描述了求解旅行商问题的蚁群算法。蚁群智能是一种群体智能方法,其中代理人(蚂蚁)使用一种叫做信息素的化学物质进行交流,这种物质会随着时间的推移而蒸发。该原则用于根据先前调查的连接找到城市之间最短的可能路线。对该算法进行评估,以获得不同数量的城市对应的小、中、大问题实例的结果。因此,随着蚁群大小和其他参数的变化,问题的大小会有所变化,以便比较不同的结果。并将蚁群算法与Kohonen和Christofides等启发式算法进行了比较。
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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