HYBRID GENETIC AND PENGUIN SEARCH OPTIMIZATION ALGORITHM (GA-PSEOA) FOR EFFICIENT FLOW SHOP SCHEDULING SOLUTIONS

被引：7

作者：

Mzili, Toufik ^{[1
,11
]}

Mzili, Ilyass ^{[2
]}

Riffi, Mohammed Essaid ^{[1
]}

Pamucar, Dragan ^{[3
,4
]}

Simic, Vladimir ^{[5
]}

Abualigah, Laith ^{[6
,7
,8
,9
]}

Almohsen, Bandar ^{[10
]}

机构：

[1] Chouaib Doukkali Univ, Fac Sci, Dept Comp Sci, EI Jadida, Morocco

[2] Hassan First Univ, Fac Econ & Management, Dept Management, Lab Res management & Dev, Settat, Morocco

[3] Univ Belgrade, Fac Org Sci, Dept Operat Res & Stat, Belgrade, Serbia

[4] Yuan Ze Univ, Coll Engn, Taoyuan, Taiwan

[5] Univ Belgrade, Fac Transport & Traff Engn, Belgrade, Serbia

[6] Al Ahliyya Amman Univ, Hourani Ctr Appl Sci Res, Amman, Jordan

[7] Middle East Univ, MEU Res Unit, Amman, Jordan

[8] Appl Sci Private Univ, Appl Sci Res Ctr, Amman, Jordan

[9] Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos, Lebanon

[10] King Saud Univ, Math Dept, Coll Sci, Riyadh, Saudi Arabia

[11] Chouaib Doukkali Univ, Fac Sci, Dept Comp Sci, Ave Jabran Khalil Jabran,BP 299 24000, El Jadida, Morocco

来源：

FACTA UNIVERSITATIS-SERIES MECHANICAL ENGINEERING | 2024年 / 22卷 / 01期

关键词：

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

D O I：

10.22190/FUME230615028M

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

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.

引用

页码：77 / 100

页数：24

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