Comparative studies on design of experiments for tuning parameters in a genetic algorithm for a scheduling problem

Arin, Arif; Rabadi, Ghaith; Unal, Resit

doi:10.1504/ijedpo.2011.040262

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Parameters Θ Cmax and Θ TFT are introduced for C max and TFT objectives, respectively, as given in Eqs. (8) and (9). According to the first acceptance rule, a neighbor solution is accepted as a new current solution if its C max is better than Θ Cmax times the current solution's C max and its TFT is better than Θ TFT times the current solution's TFT.…”

Section: Msals For Biobjective Pfss Problemsmentioning

confidence: 99%

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Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem

Alabaş-Uslu¹,

Dengız

Ağlan³

et al. 2019

Turk J Elec Eng & Comp Sci

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Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized.Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.PFSS problems are NP-hard problems even for a single objective and that is why there are numerous stateof-the-art metaheuristic algorithms for the approximate solutions of the problem. Referring interested readers to the comprehensive reviews of [3] and [4], Section 2 covers the studies that presented metaheuristic algorithms for biobjective PFSS problems. The metaheuristic algorithms given in Section 2 are all managed by a set of parameters, which has a significant impact on the solution quality and/or computational time. Searching for an * Correspondence: cigdem.uslu@marmara.edu.tr This work is licensed under a Creative Commons Attribution 4.0 International License.

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Section: Msals For Biobjective Pfss Problemsmentioning

confidence: 99%

“…Automatic algorithm configuration has increased interest in offline techniques, and it incorporates experimental design and statistical modeling techniques [5][6][7][8][9], racing algorithms [10][11][12][13], and metaoptimization approaches, which tune the parameters using any other heuristic [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem

Alabaş-Uslu¹,

Dengız

Ağlan³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

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“…Since the last decade, many researchers (e.g., [1][2][3][4][5][6] and many others) have been studying the use of different methodologies in order to summarize the process. Broadly, there is a consensus that the fine-tuning of algorithms requires a robust statistical approach, supported by efficient algorithms methods, in order to aid in the process of understanding and also in the effective settings.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Metaheuristics: An Approach Combining Response Surface Methodology and Racing Algorithms

Barbosa

Senne

Silva

2015

International Journal of Engineering Mathematics

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The setup of heuristics and metaheuristics, that is, the fine-tuning of their parameters, exercises a great influence in both the solution process, and in the quality of results of optimization problems. The search for the best fit of these algorithms is an important task and a major research challenge in the field of metaheuristics. The fine-tuning process requires a robust statistical approach, in order to aid in the process understanding and also in the effective settings, as well as an efficient algorithm which can summarize the search process. This paper aims to present an approach combining design of experiments (DOE) techniques and racing algorithms to improve the performance of different algorithms to solve classical optimization problems. The results comparison considering the default metaheuristics and ones using the settings suggested by the fine-tuning procedure will be presented. Broadly, the statistical results suggest that the fine-tuning process improves the quality of solutions for different instances of the studied problems. Therefore, by means of this study it can be concluded that the use of DOE techniques combined with racing algorithms may be a promising and powerful tool to assist in the investigation, and in the fine-tuning of different algorithms. However, additional studies must be conducted to verify the effectiveness of the proposed methodology.

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Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem

Arin

Rabadi

2017

Computers & Industrial Engineering

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Comparative studies on design of experiments for tuning parameters in a genetic algorithm for a scheduling problem

Cited by 9 publications

References 26 publications

Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem

Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem

Improving the Performance of Metaheuristics: An Approach Combining Response Surface Methodology and Racing Algorithms

Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem

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