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Finding the Shortest Hamiltonian Circuit of Selected Places in Penang Using a Generic Bee Colony Optimization Framework

机译:使用通用蜂群优化框架找到槟城选定地点的最短哈密顿回路

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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.
机译:识别最短的哈密顿回路是一项任务,出现在各种类型的工业和物流应用中。这是一个NP难题[1]。本文打算使用通用蜂群优化(BCO)框架[2],找到马来西亚槟城州选定的68个城镇/城市中最短的哈密顿回路。提出的BCO框架通过计算实现了蜜蜂的觅食过程和摇摆舞,并融合了精英主义,局部优化和自适应修剪功能。对该框架进行了修改,从而整合了过去的解决方案增强策略。而且,通过使用禁忌列表来增强局部优化方法。这项研究的结果为自然灾害发生时物流计划的准备工作提供了重要的信息。可以以一种更适当和系统的方式将援助资源一个接一个地运送到受灾地区,从而节省了成本和时间。结果表明,提出的BCO框架能够在1.32s内产生长度为263.332016km的电路(基于大圆距离)。所提出的BCO框架的性能可与遗传算法和Lin-Ker启发式算法相媲美。

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