Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (3): 91-99.DOI: 10.3778/j.issn.1002-8331.2106-0148

• Theory, Research and Development • Previous Articles     Next Articles

Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation

LI Ailian, QUAN Lingxiang, CUI Guimei, XIE Shaofeng   

  1. 1.School of Information Engineering,Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
    2.Department of Infrastructure, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
  • Online:2022-02-01 Published:2022-01-28



  1. 1.内蒙古科技大学 信息工程学院,内蒙古 包头 014010 
    2.内蒙古科技大学 基建处,内蒙古 包头 014010

Abstract: In order to address the problems that the sparrow search algorithm(SSA) has insufficient ability, loss of population diversity ,and easy to drop into local extremes in the late stage of the search, which leads to slow convergence and insufficient exploration ability of the SSA algorithm, the sparrow search algorithm integrating sine-cosine and Cauchy mutation(SCSSA) is proposed. The refracted opposition-based learning mechanism is used to initialize the population and enhance the species diversity. The sine-cosine strategy is introduced into the discoverer position update as well as a nonlinear decreasing search factor and a weighting factor to coordinate the global and local search capability of the algorithm. To improve the ability of the SSA in acquiring the global optimal solution, the Cauchy mutation is brought into the follower position to perform disturbance update to the optimal solution. The SCSSA algorithm is evaluated by 10 classical test functions in terms of convergence speed, convergence precision, average absolute error and other indexes, and the engineering design optimization problem is introduced to validate the performance of SCSSA. The experimental results prove that the improved sparrow search algorithm significantly strengthens in the convergence speed and the seeking accuracy, and exhibits better robustness.

Key words: sparrow search algorithm, refracted opposition-based learning, sine-cosine algorithm, nonlinear decreasing search factor, Cauchy mutation

摘要: 针对麻雀搜索算法(SSA)在寻优后期出现能力不足、种群多样性损失、易落进局部极值现象,造成SSA算法收敛速度慢、探索能力不足等问题,提出了融合正余弦和柯西变异的麻雀搜索算法(SCSSA)。借助折射反向学习机制初始化种群,增加物种多样性;在发现者位置更新中引入正余弦策略以及非线性递减搜索因子和权重因子协调算法的全局和局部寻优能力;在跟随者位置中引入柯西变异对最优解进行扰动更新,提高算法获取全局最优解能力。通过10个经典测试函数对SCSSA算法在收敛速度、收敛精度、平均绝对误差等指标的评估,并引进工程设计优化问题进行验证。实验结果证明改进后的麻雀搜索算法在收敛速度和寻优精度有明显增强,表现出良好的鲁棒性。

关键词: 麻雀搜索算法, 折射反向学习, 正余弦算法, 非线性递减搜索因子, 柯西变异