Optimal design of electric machine with efficient handling of constraints and surrogate assistance
Bhuvan Khoshoo, Julian Blank, Thang Q. Pham, Kalyanmoy Deb, Shanelle N. Foster
Abstract
Bhuvan Khoshooa* , Julian Blankb , Thang Q. Phama , Kalyanmoy Deba & Shanelle N. Fostera a Department of Electrical and Computer Engineering, Michigan State University, East Lansing, USAb Department of Computer Science and Engineering, Michigan State University, East Lansing, USACONTACT Bhuvan Khoshoo [email protected] data for this article can be accessed here. https://doi.org/10.1080/0305215X.2022.2152805An 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.