Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

This study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.

The optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.

To improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.

The proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.