Brushless direct current motor design using a self-adaptive JAYA optimisation algorithm

被引:2
作者
Yan, Li [1 ]
Zhang, Chuang [1 ]
Qu, Boyang [1 ]
Yu, Kunjie [2 ]
Yue, Caitong [2 ]
机构
[1] Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou, Henan, Peoples R China
[2] Zhengzhou Univ, Sch Elect Engn, Zhengzhou, Henan, Peoples R China
基金
中国国家自然科学基金;
关键词
optimisation; evolutionary computation; JAYA algorithm; brushless DC wheel motor; BRAIN STORM OPTIMIZATION; BLDC MOTOR; MODEL;
D O I
10.1504/IJBIC.2022.127501
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:139 / 149
页数:12
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