文章基本信息

标题：AN EMPIRICAL ANALYSIS OF THE RELATIONSHIP BETWEEN THE INITIALIZATION METHOD PERFORMANCE AND THE CONVERGENCE SPEED OF A META-HEURISTIC FOR FUZZY JOB-SHOP SCHEDULING PROBLEMS
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作者：IMAN MOUSA SHAHEED ; SYAIMAK ABDUL SHUKOR ; ERNA BUDHIARTI NABABAN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：93
期号：2
出版社：Journal of Theoretical and Applied
摘要：Numerous studies mentioned that the quality initial population can affect meta-heuristics convergence speed, these studies are often theoretical. However, the functionality of the initial population is extensively ignored. This overlooking may due to the literature lack of statistical evidence on the relationship between the initialization method performance and a meta-heuristic convergence speed. Therefore, this study statistically investigated aforementioned relationship by conducting an experiment and used population quality and best error rate (BRE) to gauge the performance of the state of the art initialization methods for Fuzzy Job-Shop Scheduling Problems (Fuzzy JSSPs), namely, random-based and priority rules-based methods. Thereafter, this initialization approach utilised to initiate a memetic algorithm (MA). CPU time was used to compute the MA time to reach the lower bound of 50 different sized Fuzzy JSSP instances. A Spearman's test was operated to measure the intended correlation. As a result, there was effective negative association between the initial population quality and the MA convergence speed. While, there was a dominant positive relationship between the BRE and MA convergence speed. Consequently, it is highly recommended to develop advanced initialization approaches that can generate high-quality initial population, which consists of most favourable or near to best possible solutions.
关键词：Fuzzy Job-Shop Scheduling Problems; Initialization Methods; Memetic Algorithm; Population Quality; Best Relative Error; Convergence Speed