Artificial Hunting Optimization: A Novel Method for Design Optimization of Permanent Magnet Machines

Although the design optimization of permanent magnet synchronous machines (PMSMs) has been extensively studied over the past several decades, there is limited literature available on which search algorithm is most suitable for PMSM design. To fill this gap, this article aims to propose a superior algorithm that is best suited for PMSM design. To this end, a new metaheuristic algorithm named artificial hunting optimization (AHO) is developed by combining hunting techniques and incorporating the input of human researchers. The AHO is applied to the complex permeance (CP) model for optimizing surface-mounted PM (SPM) machines. The results are compared with several classical and modern metaheuristics, such as particle swarm optimization (PSO), differential evolution (DE), gray wolf optimizer (GWO), teaching–learning-based optimization (TLBO), social group optimization (SGO), firefly algorithm (FA), and sine cosine algorithm (SCA). The test outcomes demonstrate that the AHO performs better than other existing algorithms, even with a limited number of candidate designs. Consequently, the AHO displays remarkable potential for successful application in PMSM design problems. Besides, the reliability of the CP-based analytical method is verified by finite element (FE) and experimental results.