Level-set adaptive switching method for multi-objective topology optimization of synchronous reluctance motors

Purpose This study aims to develop an efficient level-set (LS)-based multi-objective topology optimization framework capable of handling strongly nonlinear electromagnetic design problems, and to demonstrate its applicability through the design of synchronous reluctance motors. Design/methodology/approach The proposed level-set adaptive switching method (LASM) combines an LS-based topology optimization scheme with an adaptive switching mechanism of weighting coefficients. The weights are automatically determined by solving a mixed-integer linear programming problem that maximizes the expected shape variation, and switching is triggered when objective improvement stagnates, deteriorates or oscillates. This dynamic framework enables continuous exploration of Pareto fronts in multi-objective design spaces. Findings Numerical experiments demonstrate that LASM achieves broader and more uniformly distributed Pareto fronts and improved design performance compared with conventional weighted-sum optimization. The obtained geometries maintain smooth and manufacturable boundaries, confirming the practicality of the proposed framework. Originality/value LASM builds upon the LS-based switching concept of Shigematsu et al. (2022) and extends it by introducing a shape-variation-driven automatic weight computation scheme and enabling a scalable application to three or more objectives. Through these extensions, LASM eliminates designer dependency in weight setting, enhances robustness against local minima and provides a practical and fully automated framework for multi-objective electromagnetic design.