A variable neighbourhood search structure based-genetic algorithm for combinatorial optimisation problems

N. Bouhmala
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引用次数: 4

Abstract

In this paper, a variable-neighbourhood-genetic-based-algorithm is proposed for the MAX-SAT problem. Most of the work published earlier on variable neighbourhood search VNS starts from the first neighbourhood and moves on to higher neighbourhoods without controlling and adapting the ordering of neighbourhood structures. The order in which the neighbourhood structures have been proposed during the search process in this work enables the genetic algorithm with a better mechanism for performing diversification and intensification. A set of benchmark problem instances is used to compare the effectiveness of the proposed algorithm against the standard genetic algorithm. We also report promising results when the proposed algorithm is compared with state-of-the-art solvers.
一种基于可变邻域搜索结构的组合优化遗传算法
本文提出了一种基于变邻域遗传的MAX-SAT问题求解算法。先前发表的大多数关于可变邻域搜索VNS的工作从第一个邻域开始,然后移动到更高的邻域,而没有控制和适应邻域结构的顺序。本文在搜索过程中提出的邻域结构的顺序使遗传算法具有更好的执行多样化和集约化的机制。使用一组基准问题实例来比较所提出算法与标准遗传算法的有效性。当提出的算法与最先进的求解器进行比较时,我们也报告了有希望的结果。
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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