用通用蜂群优化框架寻找槟城选定地点的最短哈密顿电路

L. Wong, M. Low, C. Chong
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引用次数: 0

摘要

确定最短的哈密顿电路是出现在各种类型的工业和物流应用中的任务。这是一个np困难问题。本文拟利用通用的蜂群优化(BCO)框架[2]在马来西亚槟城选定的68个城镇/城市中寻找最短的哈密顿电路。所提出的BCO框架计算实现了蜜蜂的觅食过程和摇摆舞,并丰富了精英主义、局部优化和自适应修剪。对框架进行了修改,从而集成了过去的解决方案强化策略。同时,利用禁忌列表对局部优化方法进行了改进。本研究的结果为自然灾害发生时的物流计划的制定提供了重要的参考。援助资源可以以更适当和更系统的方式陆续送到受灾地区,从而节省成本和时间。结果表明,所提出的BCO框架能够在1.32s内生成长度为263.332016km的电路(基于大圆距离)。所提出的BCO框架的性能与遗传算法和Lin-Ker启发式算法相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finding the Shortest Hamiltonian Circuit of Selected Places in Penang Using a Generic Bee Colony Optimization Framework
Identifying the shortest Hamiltonian circuit is a task which appears in various types of industrial and logistics applications. It is a NP-hard problem [1]. This paper intends to find the shortest Hamiltonian circuit of the selected 68 towns/cities in Penang state, Malaysia using the generic Bee Colony Optimization (BCO) framework [2]. The proposed BCO framework realizes computationally the foraging process and waggle dance performed by bees and it is enriched with elitism, local optimization and adaptive pruning. A modification has been applied to the framework whereby a past solutions reinforcement policy is integrated. Also, the local optimization method is enhanced with the utilization of a Tabu list. The results from this study serve as an significant input to the preparation of logistics plan when a natural disaster occurs. Aiding resources can be delivered to affected areas, one after another, in a more appropriate and systematic manner and thus leads to cost and time saving. The results show that proposed BCO framework is able to produce a circuit (based on great-circle distance) with length of 263.332016km within 1.32s. The performance of the proposed BCO framework is comparable to the Genetic Algorithm and Lin-Ker heuristic.
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