Affiliations: [a] Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
Correspondence:
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Corresponding author: Hajime Igarashi, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan. E-mail: igarashi@ssi.ist.hokudai.ac.jp
Abstract: This paper proposes a multi-objective optimization method for permanent magnet motors using a fast optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and deep learning. Multi-objective optimization with topology optimization is effective in the design of permanent magnet motors. Although CMA-ES needs fewer population size than genetic algorithm for single objective problems, this is not evident for multi-objective problems. For this reason, the proposed method generates training data by solving the single-objective optimization multiple times using CMA-ES, and constructs a deep neural network (NN) based on the data to predict performance from motor images at high speed. The deep NN is then used for fast solution of multi-objective optimization problems. Numerical examples demonstrate the effectiveness of the proposed method.
Keywords: Deep learning, CNN, multi-objective optimization, CMA-ES, NSGA-II, PM motor
