Advanced Optimization Methods for the Design of Electromagnetic Devices

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

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