A local search-based generalized normal distribution algorithm for permutation flow shop scheduling


This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

Publication DOI: https://doi.org/10.3390/app11114837
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Funding Information: This project is funded by King Saud University, Riyadh, Saudi Arabia. Research Supporting Project number (RSP?2021/167), King Saud University, Riyadh, Saudi Arabia.
Uncontrolled Keywords: Generalized normal distribution optimization algorithm,Local search strategy,Makespan,Permutation flow shop scheduling,Materials Science(all),Instrumentation,Engineering(all),Process Chemistry and Technology,Computer Science Applications,Fluid Flow and Transfer Processes
Publication ISSN: 2076-3417
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.mdp ... 3417/11/11/4837 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-05-25
Accepted Date: 2021-05-22
Authors: Abdel-Basset, Mohamed
Mohamed, Reda
Abouhawwash, Mohamed
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Askar, S. S.



Version: Published Version

License: Creative Commons Attribution

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