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Multi-objective optimization of permanent magnet motors using deep learning and CMA-ES
- Publication Year :
- 2023
-
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.
Details
- Database :
- OAIster
- Notes :
- English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1429550098
- Document Type :
- Electronic Resource