Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 36-43.DOI: 10.11772/j.issn.1001-9081.2021010187

• Artificial intelligence • Previous Articles     Next Articles

Sparrow search algorithm based on Sobol sequence and crisscross strategy

Yuxian DUAN1,2(), Changyun LIU1   

  1. 1.Air and Missile Defense College,Air Force Engineering University,Xi’an Shaanxi 710038,China
    2.Graduate School,Air Force Engineering University,Xi’an Shaanxi 710038,China
  • Received:2021-02-02 Revised:2021-05-05 Accepted:2021-05-10 Online:2021-05-12 Published:2022-01-10
  • Contact: Yuxian DUAN
  • About author:DUAN Yuxian, born in 1992, M. S. candidate. His research interests include intelligent optimization algorithms, situation awareness.
    LIU Changyun, born in 1973, Ph. D., professor. His research interests include intelligent optimization algorithms, situation awareness.
  • Supported by:
    National Natural Science Foundation of China(61703426)


段玉先1,2(), 刘昌云1   

  1. 1.空军工程大学 防空反导学院,西安 710038
    2.空军工程大学 研究生院,西安 710038
  • 通讯作者: 段玉先
  • 作者简介:段玉先(1992—),男,山东威海人,硕士研究生,主要研究方向:智能优化算法、态势感知
    刘昌云(1973—),男, 四川泸州人,教授,博士,主要研究方向:智能优化算法、态势感知。
  • 基金资助:


For the shortcomings of falling into the local optimum easily and slow convergence in Sparrow Search Algorithm (SSA), a Sparrow Search Algorithm based on Sobol sequence and Crisscross strategy (SSASC) was proposed. Firstly, the Sobol sequence was introduced in the initialization stage to enhance the diversity and ergodicity of the population. Secondly, the nonlinear inertia weight in exponential form was proposed to improve the convergence efficiency of the algorithm. Finally, the crisscross strategy was applied to improve the algorithm. In specific, the horizontal crossover was used to enhance the global search ability, while the vertical crossover was used to maintain the diversity of the population and avoid the algorithm from trapping into the local optimum. Thirteen benchmark functions were selected for simulation experiments, and the performance of the algorithm was evaluated by Wilcoxon rank sum test and Friedman test. In comparison experiments with other metaheuristic algorithms, the mean and standard deviation generated by SSASC are always better than other algorithms when the benchmark functions extending from 10 dimensions to 100 dimensions. Experimental results show that SSASC achieves certain superiority in both convergence speed and solution accuracy.

Key words: Sparrow Search Algorithm (SSA), Sobol sequence, inertia weight, crisscross strategy, nonlinear strategy, benchmark function



关键词: 麻雀搜索算法, Sobol序列, 惯性权重, 纵横交叉策略, 非线性策略, 基准函数

CLC Number: