Multi-Objective Automatic Design of Permanent Magnet Motor Using Monte Carlo Tree Search

This study proposes a novel multi-objective design method based on Monte Carlo tree search (MCTS) for the design of permanent magnet (PM) motors. The global configurations that define the entire structure are represented by nodes in a tree structure. After MCTS is performed to select a route extending from the root to leaf node, multi-objective topology optimization (TO) is performed at the leaf node to determine the detailed shape, considering a trade-off relationship among objective functions. The novelty of this work lies in the integration of MCTS with multi-objective TO where the score of a node is provided by the number of Pareto solutions obtained by selecting that node. This enables the scoring of nodes and the determination of the node selection criterion. The proposed method is applied to the multi-objective optimization of a PM motor with respect to average torque, and either torque ripple or iron loss. The proposed method successfully obtained Pareto solutions, which comprise various global configurations and shapes.