Toufik Mzili, Ilyass Mzili, Mohammed Essaid Riffi, Dragan Pamucar, Vladimir Simic, Laith Abualigah, Bandar Almohsen

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This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics.


Hybrid Metaheuristics, PSeOA, Scheduling Problem, Combinatorial Optimization, Artificial intelligence, Swarm intelligence

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DOI: https://doi.org/10.22190/FUME230615028M


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ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4