Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control

LU Na; MA Long

doi:10.1049/cje.2021.00.217

Volume 31 Issue 4

Jul. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(4): 635-646

LU Na and MA Long, “Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 635-646, 2022, doi: 10.1049/cje.2021.00.217

Citation:

LU Na and MA Long, “Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 635-646, 2022, doi: 10.1049/cje.2021.00.217

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Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control

doi: 10.1049/cje.2021.00.217

LU Na,
MA Long

1.
School of Economics and Management, Xi’an Aeronautical University, Xi’an 710077, China

Funds: This work was supported by Soft Science Research Project of Science and Technology Department of Shaanxi Province (2019KRM093, 2021KRM154)

More Information

Author Bio:
was born in 1981. She received the Ph.D. degree from Xi’an University of Architecture and Technology, China, in 2015. Since 2015, she has been an Associate Professor Researcher at the School of Economics and Management, Xi’an Aeronautical University, China. She has published more than 20 papers. Her research interests include the complex system, computational intelligence, multiple attribute decision making, and big data modeling and simulation. (Email: hr.luna@163.com)

(corresponding author) was born in 1982. He received the B.S. degree from the School of Computer, Xidian University, Xi’an, China, in 2007, and the Ph.D. degree from the School of Management, Xi’an University of Architecture and Technology, China, in 2019. He is currently an Associate Professor with the School of Economics and Management, Xi’an Aeronautical University, China. He has authored one book, more than 15 articles, and more than 10 inventions. His research interests include computational intelligence, management of emergency materials, and big data modeling and simulation. He has a strong interest in the exploration of interdisciplinary fields of artificial intelligent and emergency management. (Email: malong1982@126.com)
Received Date: 2021-06-27
Accepted Date: 2021-09-28
Rev Recd Date: 2021-09-28

Available Online: 2021-12-06

Publish Date: 2022-07-05

Abstract

Abstract

In the traditional quantum wolf pack algorithm, the wolf pack distribution is simplified, and the leader wolf is randomly selected. This leads to the problems that the development and exploration ability of the algorithm is weak and the rate of convergence is slow. Therefore, a quantum wolf pack evolutionary algorithm of weight decision-making based on fuzzy control is proposed in this paper. First, to realize the diversification of wolf pack distribution and the regular selection of the leader wolf, a dual strategy method and sliding mode cross principle are adopted to optimize the selection of the quantum wolf pack initial position and the candidate leader wolf. Second, a new non-linear convergence factor is adopted to improve the leader wolf’s search direction operator to enhance the local search capability of the algorithm. Meanwhile, a weighted decision-making strategy based on fuzzy control and the quantum evolution computation method is used to update the position of the wolf pack and enhance the optimization ability of the algorithm. Then, a functional analysis method is adopted to prove the convergence of the quantum wolf pack algorithm, thus realizing the feasibility of the algorithm’s global convergence. The performance of the quantum wolf pack algorithm of weighted decision-making based on fuzzy control was verified through six standard test functions. The optimization results are compared with the standard wolf pack algorithm and the quantum wolf pack algorithm. Results show that the improved algorithm had a faster rate of convergence, higher convergence precision, and stronger development and exploration ability.
- Weight factor,
- Fuzzy control,
- Wolf pack evolutionary algorithm,
- Quantum evolution computation

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References(13)

References

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