Optimal design of electric machine with efficient handling of constraints and surrogate assistance
Optimal design of electric machine with efficient handling of constraints and surrogate assistance
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有效处理约束和代理辅助的电机优化设计
DOI：
10.1080/0305215x.2022.2152805
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发表时间：
2022-06
影响因子：
2.7
通讯作者：
Bhuvan Khoshoo;Julian Blank;Thang Q. Pham;K. Deb;Shanelle N. Foster
中科院分区：
文献类型：
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作者：
Bhuvan Khoshoo;Julian Blank;Thang Q. Pham;K. Deb;Shanelle N. Foster
电机优化设计问题可以归结为一个约束多目标优化问题。虽然目标需要耗时的有限元分析，但约束（例如几何约束）通常可以基于数学表达式。本文研究了这一混合优化问题，并提出了一种基于进化算法的计算效率高的优化方法。所提出的方法总是通过使用一个可推广的修复算子生成可行的解决方案，并通过将代理模型用于预测来解决耗时的目标函数。文章成功地建立了所提出的方法优于传统的优化方法。这项研究表明，一个复杂的工程设计任务，可以有效地优化多个目标和约束，需要异构的评估时间。它还展示了如何分析最佳解决方案，以选择一个首选的解决方案，并利用它来揭示最佳解决方案作为设计原则的重要设计特征。
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.