Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)
The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight fo...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/10/1107 |
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| author | Mohamed Afifi Hegazy Rezk Mohamed Ibrahim Mohamed El-Nemr |
| author_facet | Mohamed Afifi Hegazy Rezk Mohamed Ibrahim Mohamed El-Nemr |
| author_sort | Mohamed Afifi |
| collection | DOAJ |
| description | The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions. |
| first_indexed | 2024-03-10T11:27:11Z |
| format | Article |
| id | doaj.art-cc1a896a1b5b4c34976e1ac65aaeaf12 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T11:27:11Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-cc1a896a1b5b4c34976e1ac65aaeaf122023-11-21T19:34:27ZengMDPI AGMathematics2227-73902021-05-01910110710.3390/math9101107Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)Mohamed Afifi0Hegazy Rezk1Mohamed Ibrahim2Mohamed El-Nemr3Electromagnetic Energy Conversion Laboratory, Tanta University, Tanta 31527, EgyptCollege of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser 11991, Saudi ArabiaDepartment of Electromechanical, Systems and Metal Engineering, Ghent University, 9000 Ghent, BelgiumElectromagnetic Energy Conversion Laboratory, Tanta University, Tanta 31527, EgyptThe switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.https://www.mdpi.com/2227-7390/9/10/1107optimal designswitched reluctance machineMO-Jaya optimizationfinite element analysis |
| spellingShingle | Mohamed Afifi Hegazy Rezk Mohamed Ibrahim Mohamed El-Nemr Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya) Mathematics optimal design switched reluctance machine MO-Jaya optimization finite element analysis |
| title | Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya) |
| title_full | Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya) |
| title_fullStr | Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya) |
| title_full_unstemmed | Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya) |
| title_short | Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya) |
| title_sort | multi objective optimization of switched reluctance machine design using jaya algorithm mo jaya |
| topic | optimal design switched reluctance machine MO-Jaya optimization finite element analysis |
| url | https://www.mdpi.com/2227-7390/9/10/1107 |
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