An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans
This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance i...
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2025-05-01
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| author | Yanzhao Gong Richard Amankwa Adjei Guocheng Tao Yitao Zeng Chengwei Fan |
| author_facet | Yanzhao Gong Richard Amankwa Adjei Guocheng Tao Yitao Zeng Chengwei Fan |
| author_sort | Yanzhao Gong |
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| description | This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm’s search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems. |
| format | Article |
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| institution | Kabale University |
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| spelling | doaj-art-e151220bbc8f49ef9d1a006fe757c9822025-08-20T03:52:58ZengMDPI AGApplied Sciences2076-34172025-05-01159519710.3390/app15095197An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling FansYanzhao Gong0Richard Amankwa Adjei1Guocheng Tao2Yitao Zeng3Chengwei Fan4Zhejiang University—University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, ChinaSchool of Aerospace, University of Nottingham Ningbo China, Ningbo 315100, ChinaZhejiang University—University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, ChinaZhejiang University—University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, ChinaZhejiang University—University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, ChinaThis paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm’s search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems.https://www.mdpi.com/2076-3417/15/9/5197multi-objective optimizationhigh-quality population initializationassociative learningaxial cooling fanaerodynamic performance |
| spellingShingle | Yanzhao Gong Richard Amankwa Adjei Guocheng Tao Yitao Zeng Chengwei Fan An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans Applied Sciences multi-objective optimization high-quality population initialization associative learning axial cooling fan aerodynamic performance |
| title | An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans |
| title_full | An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans |
| title_fullStr | An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans |
| title_full_unstemmed | An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans |
| title_short | An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans |
| title_sort | improved multi objective grey wolf optimizer for aerodynamic optimization of axial cooling fans |
| topic | multi-objective optimization high-quality population initialization associative learning axial cooling fan aerodynamic performance |
| url | https://www.mdpi.com/2076-3417/15/9/5197 |
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