Optimal design of electric machine with efficient handling of constraints and surrogate assistance

• Published in: Engineering optimization 2024-02, Vol.56 (2), p.274-292
• Main Authors: Khoshoo, Bhuvan, Blank, Julian, Pham, Thang Q., Deb, Kalyanmoy, Foster, Shanelle N.
• Format: Article
• Language: English
• Subjects: Design engineering; Design optimization; Electric machine design; Evolutionary algorithms; Finite element method; Geometric constraints; multi-criteria decision making; multi-objective optimization; Multiple objective analysis; NSGA-II; Operators (mathematics); surrogate-assisted optimization
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2022.2152805

An optimal electric machine design task can be posed as a constrained multi-objective optimization problem. While the objectives require time-consuming finite element analysis, constraints, such as geometric constraints, can often be based on mathematical expressions. This article investigates this mixed computationally expensive optimization problem and proposes a computationally efficient optimization method based on evolutionary algorithms. The proposed method always generates feasible solutions by using a generalizable repair operator and also addresses time-consuming objective functions by incorporating surrogate models for their prediction. The article successfully establishes the superiority of the proposed method over a conventional optimization approach. This study demonstrates how a complex engineering design task can be optimized efficiently for multiple objectives and constraints requiring heterogeneous evaluation times. It also shows how optimal solutions can be analysed to select a single preferred solution and harnessed to reveal vital design features common to optimal solutions as design principles.