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Article

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

by 1, 2, 3, 4, 5, 5, 6,* and 1,*
1
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
2
Anhui CQC-CHEARI Technology Co., Ltd, Chuzhou 239000, China
3
Research Center of Big Data and Information Management, Civil Aviation Management Institute of China, Beijing 100102, China
4
Chuzhou Technical Supervision and Testing Center, Chuzhou 239000, China
5
China Household Electric Appliance Research Institute, Beijing 100176, China
6
School of Computer Science, China West Normal University, Nanchong 637002, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Giancarlo Mauri and Eui-Nam Huh
Appl. Sci. 2021, 11(23), 11192; https://doi.org/10.3390/app112311192 (registering DOI)
Received: 27 September 2021 / Revised: 15 November 2021 / Accepted: 23 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Soft Computing Application to Engineering Design)
Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.
Keywords: sparrow search algorithm; chaotic mapping; adaptive weight; t-distribution mutations; multi-strategy; global optimization sparrow search algorithm; chaotic mapping; adaptive weight; t-distribution mutations; multi-strategy; global optimization
MDPI and ACS Style

Yang, X.; Liu, J.; Liu, Y.; Xu, P.; Yu, L.; Zhu, L.; Chen, H.; Deng, W. A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation. Appl. Sci. 2021, 11, 11192. https://doi.org/10.3390/app112311192

AMA Style

Yang X, Liu J, Liu Y, Xu P, Yu L, Zhu L, Chen H, Deng W. A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation. Applied Sciences. 2021; 11(23):11192. https://doi.org/10.3390/app112311192

Chicago/Turabian Style

Yang, Xiaoxu, Jie Liu, Yi Liu, Peng Xu, Ling Yu, Lei Zhu, Huayue Chen, and Wu Deng. 2021. "A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation" Applied Sciences 11, no. 23: 11192. https://doi.org/10.3390/app112311192

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