Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm
Abstract This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions thro...
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| Format: | Article |
| Language: | English |
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Springer
2024-04-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-024-00420-z |
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| author | Sundaram B. Pandya Kanak Kalita Pradeep Jangir Ranjan Kumar Ghadai Laith Abualigah |
| author_facet | Sundaram B. Pandya Kanak Kalita Pradeep Jangir Ranjan Kumar Ghadai Laith Abualigah |
| author_sort | Sundaram B. Pandya |
| collection | DOAJ |
| description | Abstract This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (CD) coupled with an Information Feedback Mechanism (IFM) selection strategy is employed to maintain and amplify the convergence and diversity of potential solutions. MOGMO efficacy and capabilities are assessed using thirty notable case studies. This encompasses nineteen multi-objective benchmark problems without constraints, six with constraints and five multi-objective engineering design challenges. Based on the optimization results, the proposed MOGMO is better 54.83% in terms of GD, 64.51% in terms of IGD, 67.74% in terms of SP, 70.96% in terms of SD, 64.51% in terms of HV and 77.41% in terms of RT. Therefore, MOGMO has a better convergence and diversity for solving un-constraint, constraint and real-world application. Statistical outcomes from MOGMO are compared with those from Multi-Objective Equilibrium Optimizer (MOEO), Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D), Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Multi-Verse Optimization (MOMVO) and Multi-Objective Plasma Generation Optimizer (MOPGO) algorithms, utilizing identical performance measures. This comparison reveals that MOGMO consistently exhibits robustness and excels in addressing an array of multi-objective challenges. The MOGMO source code is available at https://github.com/kanak02/MOGMO . |
| first_indexed | 2024-04-24T09:49:19Z |
| format | Article |
| id | doaj.art-e487a65799ff48fa965f4f31db502b13 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2025-03-22T03:44:32Z |
| publishDate | 2024-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-e487a65799ff48fa965f4f31db502b132024-04-28T11:34:33ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832024-04-0117112910.1007/s44196-024-00420-zMulti-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective AlgorithmSundaram B. Pandya0Kanak Kalita1Pradeep Jangir2Ranjan Kumar Ghadai3Laith Abualigah4Department of Electrical Engineering, Shri K.J. PolytechnicDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and TechnologyDepartment of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical SciencesDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationComputer Science Department, Al Al-Bayt UniversityAbstract This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (CD) coupled with an Information Feedback Mechanism (IFM) selection strategy is employed to maintain and amplify the convergence and diversity of potential solutions. MOGMO efficacy and capabilities are assessed using thirty notable case studies. This encompasses nineteen multi-objective benchmark problems without constraints, six with constraints and five multi-objective engineering design challenges. Based on the optimization results, the proposed MOGMO is better 54.83% in terms of GD, 64.51% in terms of IGD, 67.74% in terms of SP, 70.96% in terms of SD, 64.51% in terms of HV and 77.41% in terms of RT. Therefore, MOGMO has a better convergence and diversity for solving un-constraint, constraint and real-world application. Statistical outcomes from MOGMO are compared with those from Multi-Objective Equilibrium Optimizer (MOEO), Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D), Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Multi-Verse Optimization (MOMVO) and Multi-Objective Plasma Generation Optimizer (MOPGO) algorithms, utilizing identical performance measures. This comparison reveals that MOGMO consistently exhibits robustness and excels in addressing an array of multi-objective challenges. The MOGMO source code is available at https://github.com/kanak02/MOGMO .https://doi.org/10.1007/s44196-024-00420-zMulti-objective optimizationEngineering design optimizationGeometric mean optimizerMOGMONon-dominated solutionPareto optimal solution |
| spellingShingle | Sundaram B. Pandya Kanak Kalita Pradeep Jangir Ranjan Kumar Ghadai Laith Abualigah Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm International Journal of Computational Intelligence Systems Multi-objective optimization Engineering design optimization Geometric mean optimizer MOGMO Non-dominated solution Pareto optimal solution |
| title | Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm |
| title_full | Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm |
| title_fullStr | Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm |
| title_full_unstemmed | Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm |
| title_short | Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm |
| title_sort | multi objective geometric mean optimizer mogmo a novel metaphor free population based math inspired multi objective algorithm |
| topic | Multi-objective optimization Engineering design optimization Geometric mean optimizer MOGMO Non-dominated solution Pareto optimal solution |
| url | https://doi.org/10.1007/s44196-024-00420-z |
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