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

Iman Mousa Shaheed, Syaimak Abdul Shukor, Erna Budhiarti Nababan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)297-311
Number of pages15
JournalJournal of Theoretical and Applied Information Technology
Volume93
Issue number2
Publication statusPublished - 30 Nov 2016

Fingerprint

Job Shop Scheduling Problem
Speed of Convergence
Empirical Analysis
Initialization
Metaheuristics
Memetic Algorithm
Convergence Speed
Error Rate
Negative Association
Priority Rules
Gages
Program processors
CPU Time
Gauge
Relationships
Job shop scheduling
Lower bound
Experiments
Experiment

Keywords

  • Best relative error
  • Convergence speed
  • Fuzzy job-shop scheduling problems
  • Initialization methods
  • Memetic algorithm
  • Population quality

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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abstract = "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.",
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AB - 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.

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