Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Chiweta Emmanuel Abunike & Ogbonnaya Inya Okoro & Sumeet S. Aphale, 2022. "Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance," Energies, MDPI, vol. 15(16), pages 1-23, August.
- Weihua Qian & Hang Xu & Houjin Chen & Lvqing Yang & Yuanguo Lin & Rui Xu & Mulan Yang & Minghong Liao, 2024. "A Synergistic MOEA Algorithm with GANs for Complex Data Analysis," Mathematics, MDPI, vol. 12(2), pages 1-30, January.
- Yan Li & Yifan Liu & Shasha Li & Leijie Qi & Jun Xie & Qing Xie, 2022. "A Novel Multi-Objective Optimal Design Method for Dry Iron Core Reactor by Incorporating NSGA-II, TOPSIS and Entropy Weight Method," Energies, MDPI, vol. 15(19), pages 1-15, October.
- Mingyu Choi & Gilsu Choi & Gerd Bramerdorfer & Edmund Marth, 2022. "Systematic Development of a Multi-Objective Design Optimization Process Based on a Surrogate-Assisted Evolutionary Algorithm for Electric Machine Applications," Energies, MDPI, vol. 16(1), pages 1-19, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
- Zeynep Adak & Mahmure Övül Arıoğlu Akan & Serol Bulkan, 0. "Multiprocessor open shop problem: literature review and future directions," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-23.
- Yepes-Borrero, Juan C. & Perea, Federico & Ruiz, Rubén & Villa, Fulgencia, 2021. "Bi-objective parallel machine scheduling with additional resources during setups," European Journal of Operational Research, Elsevier, vol. 292(2), pages 443-455.
- Chen, Shih-Hsin & Chen, Min-Chih, 2013. "Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems," International Journal of Production Economics, Elsevier, vol. 141(1), pages 24-33.
- Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
- Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
- Ciavotta, Michele & Minella, Gerardo & Ruiz, Rubén, 2013. "Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study," European Journal of Operational Research, Elsevier, vol. 227(2), pages 301-313.
- Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
- Figueira, J.R. & Liefooghe, A. & Talbi, E.-G. & Wierzbicki, A.P., 2010. "A parallel multiple reference point approach for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 205(2), pages 390-400, September.
- S. S. Panwalkar & Christos Koulamas, 2019. "The evolution of schematic representations of flow shop scheduling problems," Journal of Scheduling, Springer, vol. 22(4), pages 379-391, August.
- Pagnozzi, Federico & Stützle, Thomas, 2021. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints," Operations Research Perspectives, Elsevier, vol. 8(C).
- Zeynep Adak & Mahmure Övül Arıoğlu Akan & Serol Bulkan, 2020. "Multiprocessor open shop problem: literature review and future directions," Journal of Combinatorial Optimization, Springer, vol. 40(2), pages 547-569, August.
- Pablo Valledor & Alberto Gomez & Javier Puente & Isabel Fernandez, 2022. "Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
- Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
- Perez-Gonzalez, Paz & Framinan, Jose M., 2014. "A common framework and taxonomy for multicriteria scheduling problems with interfering and competing jobs: Multi-agent scheduling problems," European Journal of Operational Research, Elsevier, vol. 235(1), pages 1-16.
- Joshua Q. Hale & Helin Zhu & Enlu Zhou, 2020. "Domination Measure: A New Metric for Solving Multiobjective Optimization," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 565-581, July.
- Yenisey, Mehmet Mutlu & Yagmahan, Betul, 2014. "Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends," Omega, Elsevier, vol. 45(C), pages 119-135.
- Foumani, Mehdi & Smith-Miles, Kate, 2019. "The impact of various carbon reduction policies on green flowshop scheduling," Applied Energy, Elsevier, vol. 249(C), pages 300-315.
- Meiyan Lin & Kwai Sang Chin & Lijun Ma & Kwok Leung Tsui, 2020. "A comprehensive multi-objective mixed integer nonlinear programming model for an integrated elderly care service districting problem," Annals of Operations Research, Springer, vol. 291(1), pages 499-529, August.
More about this item
Keywords
optimal design; switched reluctance machine; NSGA-II optimization; finite element analysis;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:576-:d:512835. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.