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 in...

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Vydané v:Applied sciences Ročník 11; číslo 11; s. 4837
Hlavní autori: Abdel-Basset, Mohamed, Mohamed, Reda, Abouhawwash, Mohamed, Chang, Victor, Askar, S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.06.2021
Predmet:
generalized normal distribution optimization algorithm
Genetic algorithms
Heuristic
Linear programming
local search strategy
makespan
Mutation
Normal distribution
Optimization algorithms
permutation flow shop scheduling
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11114837