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Lookup NU author(s): Dr Pupong Pongcharoen, Professor Christian HicksORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper presents the first application of discrete Shark Smell Optimization (SSO) for solving multiple-row, non-identical machine layout problems. The tool, which minimizes the total rectilinear material travel distance, was applied to job shop design problems with high product variety. The work used a systematic design of experiments approach to identify appropriate parameter settings. Experiments were conducted using five benchmark datasets obtained from the literature and comparisons were made with other common nature-inspired algorithms. The results indicated that the SSO performed better than other metaheuristics for the larger problems. Novel modifications were made to the discrete SSO to further improve its performance: (i) the shark rotation movement was modified to incorporate a neighbourhood search operator; (ii) a method for selecting candidate solutions based on the flow intensity between facility pairs was included; and (iii) the number of shark rotations was increased to improve local search. The SSO was also hybridized with the Marine Predator and Whale Optimization Algorithms. The modified and hybridized SSOs were evaluated using ten datasets, which found variants that were superior up to 6.5% to existing metaheuristics and the conventional SSO. The considered strategies for modifying and hybridizing metaheuristics provide general insight into the refinement of metaheuristics.
Author(s): Wareepitak K, Sooncharoen S, Pongcharoen P, Hicks C
Publication type: Article
Publication status: Published
Journal: International Journal of Advanced Manufacturing Technology
Year: 2026
Pages: Epub ahead of print
Online publication date: 13/05/2026
Acceptance date: 25/04/2026
Date deposited: 08/05/2026
ISSN (print): 0268-3768
ISSN (electronic): 1433-3015
Publisher: Springer Nature
URL: https://doi.org/10.1007/s00170-026-18242-y
DOI: 10.1007/s00170-026-18242-y
ePrints DOI: 10.57711/zhrd-bn86
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