满足蚁群优化

Zhang Jun, Gao Wei
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引用次数: 7

摘要

蚁群系统是一种新的元启发式算法,主要用于解决组合优化问题。近年来,该算法取得了意想不到的成功,被称为蚁群优化算法。目前,人们对蚁群算法进行了一系列改进,但大多侧重于利用蚁群收集的信息来引导蚁群寻找更好的解空间,而忽视了对新路径的探索。为了扩大蚁群的搜索空间,丰富蚁群的搜索方案,提出了一种会议蚁群算法。该算法采用的主要策略是将搜索蚁对组合在一起,以扩大搜索的多样化。为了弥补相遇蚂蚁数量有限所带来的影响,采用阈值常数使算法正常运行。仿真实验证明,会议蚁群算法是解决TSP问题的最佳蚁群算法之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meeting Ant Colony Optimization
The ant system is a new meta-heuristic mainly for hard combinatorial optimization problems. It has been unexpectedly successful and known as ant colony optimization (ACO) in recent years. Nowadays, a series of improvements have been made to the ACO, most of which focus on the exploitation of gather information to guide the search of ant colony towards better solution space but neglect the exploration of new tours. In order to enlarge the ants' searching space and diversify the searching solutions, Meeting ACO is proposed here. The main strategy used in this new algorithm is to combine pairs of searching ants together to expand the diversification of the search. To make up the influence caused by limited number of meeting ants, a threshold constant is applied to make the algorithm function normally. As proved by the simulation experiments, the Meeting ACO is ranked among the best ACO for tackling the TSP problems.
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