Affiliation:
1. , Hokkaido University, , Japan
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.
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,Electronic, Optical and Magnetic Materials
Reference9 articles.
1. Multimaterial topology optimization of electric machines based on normalized Gaussian network;Sato;IEEE Trans. Magn.,2015
2. Topology optimization of synchronous reluctance motor using normalized Gaussian network;Sato;IEEE Trans. Magn.,2015
3. Novel hybridization of parameter and topology optimizations: application to permanent magnet motor;Hiruma;IEEE Trans. Magn.,2021
4. Parameter-topology hybrid optimization of electric motor with multiple permanent magnets;Hayashi;Int. J. Appl. Electromag. Mech.,2022
5. A fast and elitist multiobjective genetic algorithm: NSGA-II;Deb;IEEE Trans. Evolutionary Computation,2002