计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 317-323.doi: 10.11896/jsjkx.211100162

• 人工智能 • 上一篇    下一篇

基于双样本学习与单维搜索改进的精英麻雀搜索算法

贾凯烨1, 董砚2   

  1. 1 河北工业大学人工智能与数据科学学院 天津 300131
    2 河北工业大学电气工程学院 天津 300131
  • 收稿日期:2021-11-15 修回日期:2022-05-16 出版日期:2023-02-15 发布日期:2023-02-22
  • 通讯作者: 董砚(dongyan73@hebut.edu.cn)
  • 作者简介:(1462599039@qq.com)
  • 基金资助:
    国家自然科学基金 (U20A201284)

Improved Elite Sparrow Search Algorithm Based on Double Sample Learning and Single-dimensional Search

JIA Kaiye1, DONG Yan2   

  1. 1 School of Artificial Intelligence and Data Science,Hebei University of Technology,Tianjin 300131,China
    2 School of Electrical Engineering,Hebei University of Technology,Tianjin 300131,China
  • Received:2021-11-15 Revised:2022-05-16 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    National Natural Science Foundation of China(U20A201284)

摘要: 针对麻雀搜索算法初始种群分布不均匀,种群间信息交流少,易陷入局部最优,收敛速度慢等不足,提出了一种基于双样本学习与单维搜索改进的精英麻雀搜索算法。首先,采用Hammersley低差异序列与反向学习相结合产生精英初始种群,增强个体质量和多样性;然后,通过双样本学习策略,改进追随者的位置更新公式,加强种群间的信息交流,提高算法跳出局部最优的能力;最后,在算法迭代后期采用单维搜索模式,增强算法在后期的深度挖掘能力,提高算法的精度。通过对时间复杂度进行分析,证明了该改进未增加算法的时间复杂度。选取12个不同特征的测试函数进行寻优,测试结果表明,与其他算法相比,该算法在收敛速度、精度和稳定性上都有明显的优越性。

关键词: 麻雀搜索算法, Hammersley低差异序列, 反向学习, 双样本学习, 单维搜索

Abstract: An improved elite sparrow search algorithm based on double-sample learning and single-dimension search is proposed to solve the problems of uneven initial population distribution,little information exchange between populations,easy to fall into local optimum and slow convergence.First,the combination of Hammersley low difference sequence and reverse learning is used to generate the initial elite population to enhance individual quality and diversity.Then,the two-sample learning strategy is adop-ted to improve the follower's position updating formula,strengthen the information exchange between populations,and improve the algorithm's ability to jump out of local optimum.Finally,in the late iteration of the algorithm,the single-dimensional search mode is adopted to enhance the depth mining ability of the algorithm and improve the accuracy of the algorithm.By analyzing the time complexity,it is proved that the improved algorithm does not increase the time complexity of the algorithm.Twelve test functions with different characteristics are selected for optimization,and the test results show that the algorithm has obvious advantages in convergence speed,accuracy and stability compared with other algorithms.

Key words: Sparrow search algorithm, Hammersley low difference sequence, Reverse learning, Two-sample learning, Single-dimension search

中图分类号: 

  • TP301.6
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