A binary multi-verse optimizer algorithm applied to the set covering problem

M. Valenzuela, Alvaro Peña, Luis Lopez, H. Pinto
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引用次数: 6

Abstract

Many problems addressed in operational research are combinatorial and NP-hard type. Therefore, designing binary algorithms based on swarm intelligence continuous metaheuristics is an area of interest in operational research. In this paper we use a general binarization mechanism based on the percentile concept. We apply the percentile concept to multi-verse optimizer algorithm to solve set covering problem (SCP). Experiments are designed to demonstrate the utility of the percentile concept in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that Binary multi-verse Optimizer (BMVO) obtains adequate results when it is evaluated against another state of the art algorithm.
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一种用于集覆盖问题的二元多元优化算法
运筹学中的许多问题都是组合型和NP-hard型的。因此,设计基于群体智能连续元启发式的二元算法是运筹学研究的一个热点。在本文中,我们使用基于百分位数概念的一般二值化机制。我们将百分位概念应用到多宇宙优化算法中来解决集覆盖问题。实验旨在证明在二值化中百分位数概念的实用性。此外,我们通过基准测试实例验证了算法的效率,表明Binary multi-verse Optimizer (BMVO)在针对另一种最先进的算法进行评估时获得了足够的结果。
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