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A fast and efficient multiobjective optimization design method is developed for induction machines, which requires much fewer design iterations than the traditional design methods. In this new method, the number of prime variables that define the optimization is reduced to only six. A canonical particle swarm optimization (PSO) method with penalty function for design constraints is developed to find the optimal solution for a user-defined objective function. After several trial solutions with the PSO, the optimal regions for both the design variables and the performance indexes can be estimated. The results will provide useful information for both a drive system designer and a machine designer at an early stage of the design process. A comparison study of PSO and genetic algorithm (GA) is also performed in this paper, and the comparison shows that PSO is more successful in finding the global optima and also has better computational efficiency than GA. The original contributions of this paper are a novel induction machine design method, consideration of winding turn selection limitation, and a machine-design-focused comparison.