Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems
Abstract Complex many-objective optimization problems (MaOPs) generate multiple challenges for obtaining convergence alongside diversity within extensive multi-dimensional solution areas. Optimization approaches currently face limitations when trying to balance exploration and exploitation especiall...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00859-8 |
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| author | Pinank Patel Divya Adalja Nikunj Mashru Pradeep Jangir Arpita Reena Jangid G. Gulothungan Ahmad O. Hourani Kaznah Alshammari |
| author_facet | Pinank Patel Divya Adalja Nikunj Mashru Pradeep Jangir Arpita Reena Jangid G. Gulothungan Ahmad O. Hourani Kaznah Alshammari |
| author_sort | Pinank Patel |
| collection | DOAJ |
| description | Abstract Complex many-objective optimization problems (MaOPs) generate multiple challenges for obtaining convergence alongside diversity within extensive multi-dimensional solution areas. Optimization approaches currently face limitations when trying to balance exploration and exploitation especially when resources become limited. MaOCO represents the Many-Objective Cheetah Optimization Algorithm which draws its concepts from the hunting behavior of cheetahs. MaOCO includes adaptive search functions that use attack and sit-and-wait approaches to optimize exploration and exploitation capabilities. MaOCO produces hypervolume (HV) results that exceed NSGA-III and MaOMVO by 50% while also delivering inverse generational distance (IGD) results which reach 40% better than both competing methods. The algorithm demonstrates superior efficiency in solving complex MaOPs, because it requires lower computational costs by 15%. MaOCO successfully traverses Pareto-optimal fronts according to theoretical evaluations, and its modular structure allows for both scale-up and hybridization features. The implemented applications of this approach include optimizing energy systems along with designing structures for engineering projects. Future researchers plan to integrate MaOCO with additional metaheuristic techniques to improve its performance when dealing with dynamic and irregular Pareto front problems. |
| format | Article |
| id | doaj-art-9fb17c2f34154bd0a2ccd1ffe55df8a1 |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-9fb17c2f34154bd0a2ccd1ffe55df8a12025-08-20T03:45:32ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-06-0118116310.1007/s44196-025-00859-8Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering ProblemsPinank Patel0Divya Adalja1Nikunj Mashru2Pradeep Jangir3Arpita4Reena Jangid5G. Gulothungan6Ahmad O. Hourani7Kaznah Alshammari8Department of Mechanical Engineering, Marwadi UniversityDepartment of Mathematics, Marwadi UniversityDepartment of Mechanical Engineering, Marwadi UniversityCentre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical SciencesDepartment of Electrical and Electronics Engineering, J.J. College of Engineering and TechnologyDepartment of Electronics and Communication Engineering, SRM Institute of Science and Technology, SRM NagarHourani Center for Applied Scientific Research, Al-Ahliyya Amman UniversityDepartment of Information Technology, Faculty of Computing and Information Technology, Northern Border UniversityAbstract Complex many-objective optimization problems (MaOPs) generate multiple challenges for obtaining convergence alongside diversity within extensive multi-dimensional solution areas. Optimization approaches currently face limitations when trying to balance exploration and exploitation especially when resources become limited. MaOCO represents the Many-Objective Cheetah Optimization Algorithm which draws its concepts from the hunting behavior of cheetahs. MaOCO includes adaptive search functions that use attack and sit-and-wait approaches to optimize exploration and exploitation capabilities. MaOCO produces hypervolume (HV) results that exceed NSGA-III and MaOMVO by 50% while also delivering inverse generational distance (IGD) results which reach 40% better than both competing methods. The algorithm demonstrates superior efficiency in solving complex MaOPs, because it requires lower computational costs by 15%. MaOCO successfully traverses Pareto-optimal fronts according to theoretical evaluations, and its modular structure allows for both scale-up and hybridization features. The implemented applications of this approach include optimizing energy systems along with designing structures for engineering projects. Future researchers plan to integrate MaOCO with additional metaheuristic techniques to improve its performance when dealing with dynamic and irregular Pareto front problems.https://doi.org/10.1007/s44196-025-00859-8High-dimensional objective spacesExploration and exploitationNature-inspired optimizationConvergence and diversityMetaheuristic algorithms |
| spellingShingle | Pinank Patel Divya Adalja Nikunj Mashru Pradeep Jangir Arpita Reena Jangid G. Gulothungan Ahmad O. Hourani Kaznah Alshammari Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems International Journal of Computational Intelligence Systems High-dimensional objective spaces Exploration and exploitation Nature-inspired optimization Convergence and diversity Metaheuristic algorithms |
| title | Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems |
| title_full | Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems |
| title_fullStr | Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems |
| title_full_unstemmed | Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems |
| title_short | Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems |
| title_sort | many objective cheetah optimizer a novel paradigm for solving complex engineering problems |
| topic | High-dimensional objective spaces Exploration and exploitation Nature-inspired optimization Convergence and diversity Metaheuristic algorithms |
| url | https://doi.org/10.1007/s44196-025-00859-8 |
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