Multi-Run Genetic Algorithm Approach for Optimal Design of Concentric Reluctance Magnetic Gears

This paper presents a systematic optimization methodology for reluctance magnetic gears (RMGs) using genetic algorithms to maximize peak torque within fixed volume constraints. A multi-run parametric study with population sizes ranging from 50 to 150 individuals demonstrates robust convergence toward remarkably similar geometric proportions, with a best torque of 430.15 Nm and minimal variation (0.65%) across different configurations. The optimization redistributes radial dimensions, achieving a 95% torque improvement over uniform distribution by significantly increasing inner rotor teeth thickness by 85% while reducing modulator thickness by 74%. The most significant outcome is the emergence of a consistent design trend across all independent runs, providing general guidelines on how the RMG internal geometry should be arranged to maximize peak torque.