Development of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Location

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
H. Budi, M. Elveny, P. Zhuravlev, A. Jalil, Samaher Al-Janabi, A. Alkaim, M. Saleh, R. Shichiyakh, Sutarto
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引用次数: 5

Abstract

Abstract The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.
不均匀设施选址优化问题的自适应遗传算法的发展
不均等设施选址问题涉及到确定一组不同尺寸的生产设备的选址,以及各设备之间的相互关系。本文提出了一种求解不均匀设施布局优化问题的有效方法。该方法对遗传算法进行了改进,发展为自适应遗传算法。在该算法中,只有当每个种群中染色体的相似性达到一定水平时,才会使用突变算子。这种智能可以防止在不需要跳转的情况下跳转,并减少计算时间。为了衡量该算法的性能,将其与传统遗传算法和制冷模拟器的性能进行了比较。计算结果表明,自适应遗传算法能够获得更高质量的解。
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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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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