Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm
There is a complex coupling relationship between the structural parameters and various performance indicators of a permanent magnet eddy current coupler. In order to obtain the optimal combination of structural parameters that can improve the overall performance of the coupler, it is necessary to re...
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| Формат: | Статья |
| Язык: | English |
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MDPI AG
2023-09-01
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| Серии: | Actuators |
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| Online-ссылка: | https://www.mdpi.com/2076-0825/12/10/367 |
| _version_ | 1827722156013780992 |
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| author | Dazhi Wang Bowen Niu Pengyi Pan Guofeng Sun |
| author_facet | Dazhi Wang Bowen Niu Pengyi Pan Guofeng Sun |
| author_sort | Dazhi Wang |
| collection | DOAJ |
| description | There is a complex coupling relationship between the structural parameters and various performance indicators of a permanent magnet eddy current coupler. In order to obtain the optimal combination of structural parameters that can improve the overall performance of the coupler, it is necessary to reasonably balance the contradiction and competition among performance indicators of the permanent magnet eddy current coupler. A multi-objective optimization method for permanent magnet eddy current couplers based on scaled conjugate gradient back propagation neural network modeling, improved opposition-based learning, and normal distribution crossover operator non-dominated sorting genetic algorithm-II is proposed. The optimization results are compared with those of the traditional non-dominated sorting genetic algorithm-II and the Pareto envelope-based selection algorithm-II, and it is verified that the proposed multi-objective optimization algorithm is accurate, reliable, and has better convergence and versatility. Compared with the original model, the output torque of the optimized coupler increased by 8.54%, and the eddy current loss and cost decreased by 3.71% and 8.74%, respectively. Finally, the correctness of the theoretical analysis was verified through 3D finite element simulation and an experimental simulation platform. |
| first_indexed | 2024-03-10T21:32:42Z |
| format | Article |
| id | doaj.art-b83d657bf6af48eb8ba0e81f5a8e5a1c |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T21:32:42Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-b83d657bf6af48eb8ba0e81f5a8e5a1c2023-11-19T15:16:04ZengMDPI AGActuators2076-08252023-09-01121036710.3390/act12100367Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II AlgorithmDazhi Wang0Bowen Niu1Pengyi Pan2Guofeng Sun3College of Information Science and Engineering, Northeastern University, No. 3-11 Wenhua Road, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, No. 3-11 Wenhua Road, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, No. 3-11 Wenhua Road, Shenyang 110819, ChinaState Grid Shandong Electric Power Company Yantai Power Supply Company, No. 158 Jiefang Road, Yantai 264000, ChinaThere is a complex coupling relationship between the structural parameters and various performance indicators of a permanent magnet eddy current coupler. In order to obtain the optimal combination of structural parameters that can improve the overall performance of the coupler, it is necessary to reasonably balance the contradiction and competition among performance indicators of the permanent magnet eddy current coupler. A multi-objective optimization method for permanent magnet eddy current couplers based on scaled conjugate gradient back propagation neural network modeling, improved opposition-based learning, and normal distribution crossover operator non-dominated sorting genetic algorithm-II is proposed. The optimization results are compared with those of the traditional non-dominated sorting genetic algorithm-II and the Pareto envelope-based selection algorithm-II, and it is verified that the proposed multi-objective optimization algorithm is accurate, reliable, and has better convergence and versatility. Compared with the original model, the output torque of the optimized coupler increased by 8.54%, and the eddy current loss and cost decreased by 3.71% and 8.74%, respectively. Finally, the correctness of the theoretical analysis was verified through 3D finite element simulation and an experimental simulation platform.https://www.mdpi.com/2076-0825/12/10/367permanent magnet eddy current couplermulti-objective optimizationscaled conjugate gradient back propagation neural networknon-dominated sorting genetic algorithm-IIopposition-based learning mechanismfinite element analysis |
| spellingShingle | Dazhi Wang Bowen Niu Pengyi Pan Guofeng Sun Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm Actuators permanent magnet eddy current coupler multi-objective optimization scaled conjugate gradient back propagation neural network non-dominated sorting genetic algorithm-II opposition-based learning mechanism finite element analysis |
| title | Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm |
| title_full | Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm |
| title_fullStr | Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm |
| title_full_unstemmed | Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm |
| title_short | Multi-Objective Optimization Design of Permanent Magnet Eddy Current Coupler Based on SCG-BP Neural Network Modeling and the ONDX-NSGA-II Algorithm |
| title_sort | multi objective optimization design of permanent magnet eddy current coupler based on scg bp neural network modeling and the ondx nsga ii algorithm |
| topic | permanent magnet eddy current coupler multi-objective optimization scaled conjugate gradient back propagation neural network non-dominated sorting genetic algorithm-II opposition-based learning mechanism finite element analysis |
| url | https://www.mdpi.com/2076-0825/12/10/367 |
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