Proceedings ArticleDOI
A review of recent developments in electrical machine design optimization methods with a permanent magnet synchronous motor benchmark study
Yao Duan,Dan M. Ionel +1 more
- pp 3694-3701
TLDR
In this paper, the authors compare Response Surface (RS) and Differential Evolutionary (DE) algorithms on a permanent magnet synchronous motor (PMSM) design with 5 independent variables and a strong non-linear multi-objective Pareto front and on a function with 11 independent variables.Abstract:
The paper systematically covers the significant developments of the last decade, including surrogate modelling of electrical machines, direct and stochastic search algorithms for both single- and multi- objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing Response Surface (RS) and Differential Evolutionary (DE) algorithms on a permanent magnet synchronous motor (PMSM) design with 5 independent variables and a strong non-linear multi-objective Pareto front and on a function with 11 independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of design candidates and DE performs better when more candidates are included.read more
Citations
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Journal ArticleDOI
Modern Electrical Machine Design Optimization: Techniques, Trends, and Best Practices
TL;DR: This paper is a reference for both academics and practicing engineers regarding recent developments and future trends in electrical machine design optimization and comprises the definition of optimization scenarios regarding geometry specification and goal setting.
Journal ArticleDOI
Multidisciplinary Design Process of a 6-Slot 2-Pole High-Speed Permanent-Magnet Synchronous Machine
TL;DR: The design process described in this paper provides a straightforward procedure for the multidisciplinary design of HS permanent magnet electrical machines.
Multiobjective Optimization of Switched ReluctanceMotors Based on Design of Experiments and ParticleSwarm Optimization
TL;DR: In this article, the authors proposed a multiobjective design framework for switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches.
Journal ArticleDOI
Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives
Alexandru-Ciprian Zvoianu,Gerd Bramerdorfer,Edwin Lughofer,Siegfried Silber,Wolfgang Amrhein,Erich Peter Klement +5 more
TL;DR: The key and novel aspect of this optimization procedure is the on-the-fly automated creation of highly accurate and stable surrogate fitness functions based on artificial neural networks (ANNs) in the middle and end parts of the NSGA-II run (->hybridization) in order to significantly reduce the very high computational effort required by the optimization process.
Journal ArticleDOI
Multiobjective Optimization of a Double-Side Linear Vernier PM Motor Using Response Surface Method and Differential Evolution
TL;DR: This paper designs a double-side linear vernier permanent-magnet motor, which incorporates the merits of high thrust force capability, high power factor, and high efficiency and proposes a framework of multiobjective optimization to improve the overall motor performances.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Book
An Introduction to Genetic Algorithms
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Book
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
TL;DR: This volume explores the differential evolution (DE) algorithm in both principle and practice and is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
Journal ArticleDOI
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Book
Differential Evolution: A Practical Approach to Global Optimization
TL;DR: The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast as discussed by the authors, which is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimisation.
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