Efficiency Improvement of Permanent Magnet Motor Using Swarm Intelligence Algorithms

Abstract

This paper presents a novel approach to the efficiency improvement of permanent magnet synchronous motor using several swarm intelligence algorithms as an optimization tool. In this research work the following algorithms are used: particle swarm optimization, cuckoo search, grey wolf algorithm and dragonfly optimization algorithm. They all belong to the so called meta-heuristic optimization group of algorithms, which so far have proven to be quite suitable for optimization of standard mathematical functions. The idea is to implement those novel optimization algorithms for the efficiency improvement of permanent magnet synchronous motor, where the objective function in the optimization process is the efficiency of the investigated motor. Comparative analysis of the initial and the optimal solutions gained from the optimizations using different swarm intelligence algorithms is performed.