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.
Highlights
The optimization problem is a common real-world problem that requires seeking the maximum or minimum value of a given objective function and they can be classified as single-objective optimization problems and multi-objective optimization problems [1,2]
The chaotic mapping is used to generate the values of the parameter R2
The logarithmic spiral search strategy is used to expand the search of sparrow search algorithm (SSA) to the surrounding area, enhancing the population diversity and avoiding falling into local optimum
Summary
The optimization problem is a common real-world problem that requires seeking the maximum or minimum value of a given objective function and they can be classified as single-objective optimization problems and multi-objective optimization problems [1,2]. Chen et al [27] enhanced the performance of neighborhood search algorithm by introducing ad hoc destroy/repair heuristics and a periodic perturbation procedure, with successful solution of the dynamic vehicle routing problem Wang et al [28] proposed a new newsvendor model and apply a histogram-based distribution estimation algorithm to solve it. SSA is a new swarm-based optimization algorithm based on sparrow foraging principle proposed by XUE in 2020 [30], which has the advantages of simple structure and few control parameters. Based on the discussion above, a chaos sparrow search algorithm based on logarithmic spiral search strategy and adaptive step size strategy (CLSSA) is proposed in this paper, which employs three strategies to enhance the global search ability of SSA.
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