Abstract
The multiobjective design optimization generally includes the conflicting objectives and the use of conventional design optimization for MO problem does not so good approach to obtain an effective optimal solution. In this article, genetic algorithm is used to solve such optimal multiobjective design of axial flux permanent magnet (AFPM) motor. In order to effectively obtain a set of Pareto optimal solutions, ranking method is applied. The objective functions are decrease of cogging torque, increase of torque respectively. The airgap length and teeth angle are selected for the design variables. Some experimental results are compared with the simulation ones for the validity.
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