Brushless direct current motor design using a self-adaptive JAYA optimisation algorithm Online publication date: Wed, 07-Dec-2022
by Li Yan; Chuang Zhang; Boyang Qu; Kunjie Yu; Caitong Yue
International Journal of Bio-Inspired Computation (IJBIC), Vol. 20, No. 3, 2022
Abstract: This paper proposes a self-adaptive JAYA (SAJAYA) to maximise the efficiency of brushless direct current (BLDC) motor by optimising the design parameters. In the proposed SAJAYA, a new hybrid learning strategy is designed to maintain the diversity and avoid premature convergence. Further, a self-adaptive selection mechanism is developed based on the evolutionary state of the individuals, in order to automatically assign the original learning strategy of JAYA or the new hybrid learning strategy to each individual. In this manner, the exploration and exploitation abilities of the SAJAYA are expected to be balanced. In addition, an adaptive weight strategy is introduced to the original JAYA to control the degree of the individuals approaching the best solution and avoiding the worst solution during the different evolution stages. Experimental results show that the proposed SAJAYA shows a superior performance in solving the BLDC motor optimisation problem compared with other well-established algorithms.
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