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Article type: Research Article
Authors: Zhu, Xuemina | Liu, Shenga; * | Zhu, Xuelinb | You, Xiaomingc
Affiliations: [a] School of Management, Shanghai University of Engineering Science, Shanghai, China | [b] School of Information, University of International Business and Economics, Beijing, China | [c] College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
Correspondence: [*] Corresponding author. Sheng Liu, School of Management, Shanghai University of Engineering Science, Shanghai, China. E-mail: ls6601@163.com.
Abstract: An enhancing sparrow optimization algorithm with hybrid multi-strategy (EGLTA-SSA) is proposed, to improve the defects of the sparrow search algorithm (SSA), which is easy to fall into local optimum. Firstly, the elite backward learning strategy is introduced to initialize the sparrow population, to generate high-quality initial solutions. Secondly, the leader position is updated by fusing multi-strategy mechanisms. On one hand, the high distributivity of arithmetic optimization algorithm operators are used to deflate the target position, and enhance the ability of SSA to jump out of the local optimum. On the other hand, the leader position is perturbed by adopting the golden levy flight method and the t-distribution perturbation strategy to improve the shortcoming of SSA in the late iteration when the population diversity decreases. Further, a probability factor is added for random selection to achieve more effective communication among leaders. Finally, to verify the effectiveness of EGLTA-SSA, CEC2005 and CEC2019 functions are tested and compared with state-of-the-art algorithms, and the experimental results show that EGLTA-SSA has a better performance in terms of convergence rate and stability. EGLTA-SSA is also successfully applied to three practical engineering problems, and the results demonstrate the superior performance of EGLTA-SSA in solving project optimization problems.
Keywords: Sparrow optimization algorithm (SSA), arithmetic optimization algorithm, golden levy flight distribution, t-distribution perturbation, engineering design problems
DOI: 10.3233/JIFS-231114
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5601-5632, 2023
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