Performance evaluation and comparison of multi-objective optimization algorithms for the analytical design of switched reluctance machines

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines. The multi-physics and multi-objective nature of electric machine design problems are discussed, followed by benchmark studies comparing generic algorithms (GA), differential evolution (DE) algorithms and particle swarm optimizations (PSO) on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front. To better quantify the quality of the Pareto fronts, five primary quality indicators are employed to serve as the algorithm testing metrics. The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately, a significant amount of candidate designs. However, DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered