HYBRID GENETIC AND PENGUIN SEARCH OPTIMIZATION ALGORITHM (GA-PSEOA) FOR EFFICIENT FLOW SHOP SCHEDULING SOLUTIONS
Toufik Mzili, Ilyass Mzili, Mohammed Essaid Riffi, Dragan Pamučar, Vladimir Šimić, Laith Abualigah, Bandar Almohsen
Abstract
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.
Topics & Concepts
Flow shop schedulingComputer scienceGenetic algorithmMathematical optimizationScheduling (production processes)Job shop schedulingAlgorithmParallel computingMathematicsScheduleOperating systemScheduling and Optimization AlgorithmsAdvanced Manufacturing and Logistics Optimization