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Most studies on permanent magnet synchronous motors (PMSMs) focus on either interior type or surface mounted magnet configurations and their design changes. This paper deals with the improvements on PMSMs' efficiency and performance after Halbach Array implementation by using meta-heuristic optimization algorithms. Analytical and numerical modeling of the motor are achieved and finite element method (FEM) is used for the numerical models. Particle swarm optimization & genetic algorithm were performed for the optimal design of the geometrical parameters of the permanent magnet (PM). The analytical and numerical results of the proposed design are compared to the analytical and/or numerical results of the reference motor. The improvements in efficiency, power/weight ratio, and cost are achieved with the optimized motor structure for the same motor requirements. [ABSTRACT FROM AUTHOR]

This paper presents multiobjective fuzzy genetic algorithm optimization approach to a submersible motor design. Utilizing the concept of fuzzy sets and convex fuzzy decision making, the motor design task is formulated as a multiobjective fuzzy optimization problem and solved using a genetic algorithm. The two-dimensional Finite Element Method (FEM) is then used to confirm the validity of the optimal design. The optimization results show the effectiveness and achievement of the proposed method. [ABSTRACT FROM AUTHOR]