Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (16): 71-83.DOI: 10.3778/j.issn.1002-8331.2202-0134

• Theory, Research and Development • Previous Articles     Next Articles

Improved Sparrow Search Algorithm Based on Multi-Strategy Mixing

HUI Lichuan, CHEN Xuelian, MENG Sibo   

  1. School of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Online:2022-08-15 Published:2022-08-15



  1. 辽宁工程技术大学 电气与控制工程学院,辽宁 葫芦岛 125105

Abstract: Dedicated to tackling the shortcomings of the simple sparrow search algorithm(SSA) with inadequate search area, sluggish convergence speed and convenient to crumple into partial top of the line when dealing with complicated optimization problems, an improved sparrow search algorithm based on multi-strategy mixing(IMSSA) is proposed. The sparrow individual position is initialized by the usage of Sine chaotic map, which enriches the vary of the population and compensates for the uneven population distribution and inadequate search space. The diversity global optimal guidance strategy with inertia weight is adopted to promote the convergence speed and regulate the overall search and local exploitation ability of the algorithm. The double-sample learning strategy is used which enables the algorithm soar out of the local optimum and enhance the population’s search capability of the solution space. The algorithm is simulated via test functions, and the effectiveness of three improved strategies is verified, as well as Wilcoxon rank sum test and time complexity evaluation have been carried out. The effects point out that the overall performance of IMSSA is notably improved. Finally, the algorithm is used to optimize the parameters of support vector machine and establish the bearing fault diagnosis model which confirms the validity of the modified strategy.

Key words: sparrow search algorithm, Sine chaotic map, inertia weight, global optimal guidance, double-sample learning, bearing fault

摘要: 针对基本麻雀搜索算法(sparrow search algorithm,SSA)在处理复杂优化问题时存在的搜索空间不足、收敛速度慢和易陷入局部最优等问题,提出一种多策略混合的改进麻雀搜索算法(improved sparrow search algorithm based on multi-strategy mixing,IMSSA)。利用Sine混沌映射初始化麻雀个体位置,丰富种群多样性,解决种群分布不均匀、搜索空间不足等问题;引入带有惯性权重的多样性全局最优引导策略来加快收敛速度,调控算法的全局探索与局部开发能力;采用双样本学习策略使算法跳出局部最优,提高种群对解空间的搜索能力。通过测试函数对算法进行仿真实验,验证三种改进策略的有效性,并且进行Wilcoxon秩和检验和时间复杂度分析,结果表明IMSSA算法的各项性能均有显著提升。最后用算法优化支持向量机参数,建立轴承故障诊断模型,进一步证明了改进策略是可行有效的。

关键词: 麻雀搜索算法, Sine混沌映射, 惯性权重, 全局最优引导, 双样本学习, 轴承故障