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Applied Sciences
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Advanced Optimization Methods for the Design of Electromagnetic Devices
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Advanced Optimization Methods for the Design of Electromagnetic Devices

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 16274

Special Issue Editors

Dr. Gang Lei

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: computational electromagnetics; advanced electrical machines and drive systems for electric vehicles; optimal energy management systems for microgrids and virtual power plants; multidisciplinary design optimization methods based on AI and cloud services
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianguo Zhu

E-Mail Website
Co-Guest Editor
School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: computational electromagnetics; measurement and modeling of magnetic properties of materials; electrical machines and drives; power electronics; renewable energy systems; smart microgrids; digital energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Shuangxia Niu

E-Mail Website
Co-Guest Editor
Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Interests: renewable energy; motor drive and machine design
Prof. Dr. Xiaodong Sun

E-Mail Website
Co-Guest Editor
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Interests: electric vehicles; drives and control for electric vehicles; motor drives and control; bearingless motors; magnetic bearings and intelligent control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weinong Fu

E-Mail Website
Co-Guest Editor
Department of Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: computational electromagnetics; optimal design of electric devices; applied electromagnetics; novel electric machines
Prof. Dr. Shuhong Wang

E-Mail
Co-Guest Editor
Xi’an Jiaotong University
Interests: electromagnetics; design optimization of electromagnetic devices; advanced electrotechnical materials, and superconducting power technology

Special Issue Information

Dear Colleagues,

Electromagnetic devices have been widely employed in numerous domestic appliances, biomedical instruments, and industrial equipment and systems, for example, as electrical drive systems for air conditioners, artificial hearts, and electrical vehicles (EVs) as well as in wireless power transmission systems for mobile and EV battery charging. To improve their performance, e.g., through higher efficiency, optimization is always a necessary step in the design process. Optimization of electromagnetic devices has been an active research topic of several international conferences, including COMPUMAG, CEFC, INTERMAG, ICEMS, and ECCE. Extensive research work has allowed many optimization methods to be employed and, in some cases, new methods have been developed for the design of electromagnetic devices and systems, such as multi-objective, multi-level, and multidisciplinary optimization methods. The performance of electromagnetic devices can be theoretically improved using these methods. However, as the number of design parameters/objectives and complexity of analysis model increase, high optimization efficiency becomes a serious challenge for many design scenarios, e.g., the optimization of machines and drive systems for EVs and maglevs. Furthermore, the practical performance of electromagnetic devices is also affected significantly by the inevitable material diversities and uncertainties in the manufacturing process. Therefore, reliability-based and robust optimizations have recently attracted significant research attention. This topic is of ever-growing significance for smart manufacturing in the context of industry 4.0. Advanced technologies, such as AI, industrial big data, and cloud computing, will greatly benefit the handling of these optimization problems.

This Special Issue aims to present a collection of scientific manuscripts covering the theoretical and practical aspects associated with optimization methods for electromagnetic devices and systems. The state-of-the-art and any emerging developments in this field are welcome. Topics may include, but are not limited to, the following:

  • Improved/advanced intelligent optimization algorithms
  • Surrogate models and design of experiment techniques
  • Multi-objective optimization methods
  • Multi-level optimization methods
  • Multidisciplinary optimization methods
  • Reliability-based optimization methods
  • Robust optimization methods
  • Parallel computing and optimization
  • Optimization methods based on cloud computing technologies
  • AI-based optimization methods for electromagnetic devices and systems

Dr. Gang Lei
Prof. Dr. Weinong Fu
Prof. Dr. Jianguo Zhu
Prof. Dr. Shuangxia Niu
Prof. Dr. Xiaodong Sun
Prof. Dr. Shuhong Wang;

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization methods
  • electromagnetic devices
  • surrogate models
  • intelligent optimization algorithms
  • multi-objective optimization
  • multidisciplinary optimization
  • robust optimization

Published Papers (3 papers)

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Research

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16 pages, 1126 KiB  
Article
An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals
by Chao Jiang, Pruthvi Serrao, Mingjie Liu and Chongdu Cho
Appl. Sci. 2020, 10(15), 5110; https://doi.org/10.3390/app10155110 - 25 Jul 2020
Cited by 6 | Viewed by 2185
Abstract
Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. [...] Read more.
Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advanced Optimization Methods for the Design of Electromagnetic Devices)
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Review

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24 pages, 13130 KiB  
Review
Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions
by Yanbin Li, Gang Lei, Gerd Bramerdorfer, Sheng Peng, Xiaodong Sun and Jianguo Zhu
Appl. Sci. 2021, 11(4), 1627; https://doi.org/10.3390/app11041627 - 11 Feb 2021
Cited by 43 | Viewed by 6843
Abstract
This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is [...] Read more.
This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices. Full article
(This article belongs to the Special Issue Advanced Optimization Methods for the Design of Electromagnetic Devices)
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33 pages, 2861 KiB  
Review
Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems
by Tamás Orosz, Anton Rassõlkin, Ants Kallaste, Pedro Arsénio, David Pánek, Jan Kaska and Pavel Karban
Appl. Sci. 2020, 10(19), 6653; https://doi.org/10.3390/app10196653 - 23 Sep 2020
Cited by 86 | Viewed by 6530
Abstract
The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum [...] Read more.
The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies. Full article
(This article belongs to the Special Issue Advanced Optimization Methods for the Design of Electromagnetic Devices)
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