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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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025004529 |
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| author | Mohammad Aljaidi Janjhyam Venkata Naga Ramesh Ajmeera Kiran Pradeep Jangir Arpita Sundaram B. Pandya Wulfran Fendzi Mbasso Laith Abualigah Ali Fayez Alkoradees Mohammad Khishe |
| author_facet | Mohammad Aljaidi Janjhyam Venkata Naga Ramesh Ajmeera Kiran Pradeep Jangir Arpita Sundaram B. Pandya Wulfran Fendzi Mbasso Laith Abualigah Ali Fayez Alkoradees Mohammad Khishe |
| author_sort | Mohammad Aljaidi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6faabedc02f84e79b88b3d6bc6fd543f |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-6faabedc02f84e79b88b3d6bc6fd543f2025-08-20T02:55:12ZengElsevierResults in Engineering2590-12302025-03-012510437210.1016/j.rineng.2025.104372MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problemsMohammad Aljaidi0Janjhyam Venkata Naga Ramesh1Ajmeera Kiran2Pradeep Jangir3 Arpita4Sundaram B. Pandya5Wulfran Fendzi Mbasso6Laith Abualigah7Ali Fayez Alkoradees8Mohammad Khishe9Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan; Corresponding authors.Department of CSE, Graphic Era Hill University, Dehradun 248002, India; Department of CSE, Graphic Era Deemed To Be University, Dehradun 248002, Uttarakhand, IndiaDepartment of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana 500043, IndiaUniversity 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, IndiaDepartment of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602 105, IndiaDepartment of Electrical Engineering, Shri K.J. Polytechnic, Bharuch 392 001, IndiaTechnology and Applied Sciences Laboratory, U.I.T of Douala, P.O Box: 8689- Douala, University of Douala, CameroonComputer Science Department, Al al-Bayt University, Mafraq 25113, JordanUnit of Scientific Research, Applied College, Qassim University, Saudi Arabia; Corresponding authors.Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran; Jadara University Research Center, Jadara University, Irbid, Jordan; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2590123025004529Multi-objective optimizationMany-objective optimizationMany-objective salp swarm algorithmReference pointEngineering design problems |
| spellingShingle | Mohammad Aljaidi Janjhyam Venkata Naga Ramesh Ajmeera Kiran Pradeep Jangir Arpita Sundaram B. Pandya Wulfran Fendzi Mbasso Laith Abualigah Ali Fayez Alkoradees Mohammad Khishe MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems Results in Engineering Multi-objective optimization Many-objective optimization Many-objective salp swarm algorithm Reference point Engineering design problems |
| title | MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems |
| title_full | MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems |
| title_fullStr | MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems |
| title_full_unstemmed | MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems |
| title_short | MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems |
| title_sort | maossa a new high efficiency many objective salp swarm algorithm with information feedback mechanism for industrial engineering problems |
| topic | Multi-objective optimization Many-objective optimization Many-objective salp swarm algorithm Reference point Engineering design problems |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025004529 |
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