Open Access
ARTICLE
A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems
Andi Tang, Huan Zhou*, Tong Han, Lei Xie
Aeronautics Engineering College, Air Force Engineering University, Xi’an, 710038, China
* Corresponding Author:Huan Zhou. Email: kgy
(This article belongs to this Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
Computer Modeling in Engineering & Sciences 2022, 130(1), 331-364. https://doi.org/10.32604/cmes.2021.017310
Received 30 April 2021; Accepted 15 July 2021; Issue published 29 November 2021
Abstract
The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the
sparrow foraging principle. Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence
speed and difficulty in jumping out of the local optimum. In order to overcome these shortcomings, a chaotic
sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in
this paper. Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is
introduced to adjust the main parameters of SSA. Secondly, in order to improve the diversity of the population and
enhance the search of the surrounding space, the logarithmic spiral strategy is introduced to improve the sparrow
search mechanism. Finally, the adaptive step strategy is introduced to better control the process of algorithm
exploitation and exploration. The best chaotic map is determined by different test functions, and the CLSSA
with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems. The
simulation results show that the iterative map is the best chaotic map, and CLSSA is efficient and useful for
engineering problems, which is better than all comparison algorithms.
Keywords
Cite This Article
Tang,, A., Han, T., Xie, L. (2022). A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems.
CMES-Computer Modeling in Engineering & Sciences, 130(1), 331–364.
Citations