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 zhouh@163.com
(This article belongs to this Special Issue:Swarm Intelligence and Applications in Combinatorial Optimization)
Received 30 April 2021; Accepted 15 July 2021 ; Published online 07 September 2021
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.
Sparrow search algorithm; global optimization; adaptive step; benchmark function; chaos map