MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems

Mohammad Aljaidi; Janjhyam Venkata Naga Ramesh; Ajmeera Kiran; Pradeep Jangir; Arpita; Sundaram B. Pandya; Wulfran Fendzi Mbasso; Laith Abualigah; Ali Fayez Alkoradees; Mohammad Khishe

Results in Engineering (Mar 2025)

MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems

Mohammad Aljaidi,
Janjhyam Venkata Naga Ramesh,
Ajmeera Kiran,
Pradeep Jangir,
Arpita,
Sundaram B. Pandya,
Wulfran Fendzi Mbasso,
Laith Abualigah,
Ali Fayez Alkoradees,
Mohammad Khishe

Affiliations

Mohammad Aljaidi: Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan; Corresponding authors.
Janjhyam Venkata Naga Ramesh: Department of CSE, Graphic Era Hill University, Dehradun 248002, India; Department of CSE, Graphic Era Deemed To Be University, Dehradun 248002, Uttarakhand, India
Ajmeera Kiran: Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana 500043, India
Pradeep Jangir: University Centre for Research and Development, Chandigarh University, Gharuan, Mohali 140413, India; Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Arpita: Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602 105, India
Sundaram B. Pandya: Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch 392 001, India
Wulfran Fendzi Mbasso: Technology and Applied Sciences Laboratory, U.I.T of Douala, P.O Box: 8689- Douala, University of Douala, Cameroon
Laith Abualigah: Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan
Ali Fayez Alkoradees: Unit of Scientific Research, Applied College, Qassim University, Saudi Arabia; Corresponding authors.
Mohammad Khishe: Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran; Jadara University Research Center, Jadara University, Irbid, Jordan; Corresponding authors.

Journal volume & issue: Vol. 25
p. 104372

Abstract

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The pursuit of convergence in multi-objective optimization usually results in population clustering that produces suboptimal outcomes for both convergence and diversity performance. This paper introduces MaOSSA as a new Many-Objective Salp Swarm Algorithm which combines reference point strategies with niche preservation and Information Feedback Mechanism (IFM). The strategy enables control of convergence and diversity while simultaneously adapting to alterations in the Pareto front. The algorithm achieves personal diversity through its edge individual preservation strategy and density estimation method which maintains uniform population diversity. The evaluation of MaOSSA included DTLZ1-DTLZ7 benchmark problems and five real-world engineering design problems (RWMaOP1–RWMaOP5) that contained 5 to 15 objectives. The performance evaluation between MaOSCA, MaOPSO, NSGA-III, and MaOMFO algorithms showed that MaOSSA delivered superior outcomes regarding Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), Spread (SD), Hypervolume (HV), and Runtime (RT). The experimental outcomes show MaOSSA delivers superior performance than current methods by achieving optimal convergence-diversity balance which establishes it as an efficient solution for many-objective optimization tasks.

Published in Results in Engineering

ISSN: 2590-1230 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Technology
Website: https://www.journals.elsevier.com/results-in-engineering

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