Optimal Design of Electric Machine with Efficient Handling of Constraints and Surrogate Assistance

TL;DR

This paper presents an advanced multi-objective optimization method for electric machine design that efficiently handles heterogeneous evaluation times by combining surrogate models with a custom repair operator within NSGA-II.

Contribution

It introduces a novel optimization approach that integrates surrogate modeling and a repair operator to effectively manage expensive and inexpensive constraints in electric machine design.

Findings

Proven superiority over conventional methods in optimization efficiency.

Successfully optimized complex multi-objective electric machine designs.

Identified common design principles from optimal solutions.

Abstract

Electric machine design optimization is a computationally expensive multi-objective optimization problem. While the objectives require time-consuming finite element analysis, optimization constraints can often be based on mathematical expressions, such as geometric constraints. This article investigates this optimization problem of mixed computationally expensive nature by proposing an optimization method incorporated into a popularly-used evolutionary multi-objective optimization algorithm - NSGA-II. The proposed method exploits the inexpensiveness of geometric constraints to generate feasible designs by using a custom repair operator. The proposed method also addresses the time-consuming objective functions by incorporating surrogate models for predicting machine performance. The article successfully establishes the superiority of the proposed method over the conventional optimization approach. This study clearly demonstrates how a complex engineering design can be optimized for multiple objectives and constraints requiring heterogeneous evaluation times and optimal solutions can be analyzed to select a single preferred solution and importantly harnessed to reveal vital design features common to optimal solutions as design principles.