Abstract
The paper presents an effective evolutionary method to the optimum design of three-phase induction motor using adaptive particle swarm optimization (APSO) technique. To avoid premature convergence of the classical PSO algorithm, the parameters such as inertia weight factor and acceleration factors are made adaptive on the basis of objective functions of the current and best solutions. The optimization algorithm considers the annual cost of the motor including the power loss cost as objective function and six important motor performance indices as inequality constraints. These functions are expressed in terms of motor design variables. The APSO integrates penalty parameter-less constraint handling strategy for handling the constraints. The potential of the proposed approach has been tested on two sample motors, and the results are compared with genetic algorithm, classical PSO and conventional design methods. It is observed that the proposed method is superior in terms of solution quality, robustness and computational efficiency.
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