Soft Computing Using an HPSOGWO variant for Functions Optimization and PMSM Drive Control

In this paper, an improvement on the nature-inspired hybrid algorithm HPSOGWO is proposed. This latter hybrid algorithm is a combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The newly modified HPSOGWO is designed to enhance the accuracy of convergence. First, the MHPSOGWO is implied to optimize various unimodal and multimodal test functions for different dimensions. Second, an optimization of the gains of a proportional and integral corrector in a Permanent Magnet Synchronous Motor (PMSM) vector control system is described. Consequently, numerical, statistical, and graphical results reveal that the enhanced variation is slightly better the HPSOGWO, PSO, and GWO techniques regarding outcomes quality and capacity to locate the global optimum.