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

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

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 wi...

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Main Authors: Mohammad Aljaidi, Janjhyam Venkata Naga Ramesh, Ajmeera Kiran, Pradeep Jangir, Arpita, Sundaram B. Pandya, Wulfran Fendzi Mbasso, Laith Abualigah, Ali Fayez Alkoradees, Mohammad Khishe
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Engineering
Subjects:
Multi-objective optimization
Many-objective optimization
Many-objective salp swarm algorithm
Reference point
Engineering design problems
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025004529
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Summary: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.
ISSN:2590-1230

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