Support vector machine optimization via an improved elephant herding algorithm for motor energy efficiency rating

Xinrui Ren; Jianbo Yu; Zhaomin Lv; Xinrui Ren; Jianbo Yu; Zhaomin Lv

doi:10.3934/mbe.2022557

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 11957-11982. doi: 10.3934/mbe.2022557

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Support vector machine optimization via an improved elephant herding algorithm for motor energy efficiency rating

1.
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2.
State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
3.
School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
Academic editor: Hamid Reza Karimi

Received: 23 June 2022 Revised: 18 July 2022 Accepted: 19 July 2022 Published: 17 August 2022

Accurate evaluation of motor energy efficiency under off-condition operation can provide an important basis for an energy-saving upgrade of the motor and the elimination of backward motors. By considering the power quality, motor characteristics and load characteristics, a motor energy efficiency evaluation system with seven indexes and 10 grades was constructed. An improved elephant herding optimization method combined with a support vector machine rating model is proposed, it achieved an accuracy higher than 98%. Considering the slow convergence speed and low convergence precision of the standard elephant herding optimization (EHO) method, it is easy to fall into the local optimum problem. To improve population initialization, chaotic mapping and adversarial learning were used to achieve EHO with population diversity and global search capability. Group learning and elite retention have been added to improve the local development ability of the algorithm. The improved EHO has been compared with other intelligent optimization algorithms by using 12 benchmark functions, and the results show that the improved algorithm has better optimization performance.
- motor energy efficiency grading,
- improved elephant herding optimization,
- support vector machine
Citation: Xinrui Ren, Jianbo Yu, Zhaomin Lv. Support vector machine optimization via an improved elephant herding algorithm for motor energy efficiency rating[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 11957-11982. doi: 10.3934/mbe.2022557

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Abstract

Accurate evaluation of motor energy efficiency under off-condition operation can provide an important basis for an energy-saving upgrade of the motor and the elimination of backward motors. By considering the power quality, motor characteristics and load characteristics, a motor energy efficiency evaluation system with seven indexes and 10 grades was constructed. An improved elephant herding optimization method combined with a support vector machine rating model is proposed, it achieved an accuracy higher than 98%. Considering the slow convergence speed and low convergence precision of the standard elephant herding optimization (EHO) method, it is easy to fall into the local optimum problem. To improve population initialization, chaotic mapping and adversarial learning were used to achieve EHO with population diversity and global search capability. Group learning and elite retention have been added to improve the local development ability of the algorithm. The improved EHO has been compared with other intelligent optimization algorithms by using 12 benchmark functions, and the results show that the improved algorithm has better optimization performance.

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