兰州理工大学学报

兰州理工大学学报 ›› 2025, Vol. 51 ›› Issue (2): 78-87.

• 自动化技术与计算机技术 • 上一篇    下一篇

基于自适应t分布的改进麻雀搜索算法及其应用

赵小强*1,2,3, 顾鹏1   

  1. 1.兰州理工大学 电气工程与信息工程学院, 甘肃 兰州 730050;
    2.兰州理工大学 甘肃省工业过程先进控制重点实验室, 甘肃 兰州730050;
    3.兰州理工大学 国家级电气与控制工程实验教学中心, 甘肃 兰州 730050
  • 收稿日期:2022-07-21 出版日期:2025-04-28 发布日期:2025-04-29
  • 通讯作者: 赵小强(1969-),男,陕西岐山人,博士,教授,博导.Email:xqzhao@lut.edu.cn
  • 基金资助:
    国家自然科学基金(62263021),甘肃省高校产业支撑计划项目(2023CYZC-24)

Improved sparrow search algorithm based on adaptive t distribution and its applications

ZHAO Xiao-qiang1,2,3, GU Peng1   

  1. 1. College of Electrical Engineering and Information Engineering,Lanzhou University of Technology, Lanzhou 730050, China;
    2. Key Laboratory of Advanced Control of Industrial Processes of Gansu Province, Lanzhou University of Technology, Lanzhou 730050, China;
    3. National Electrical and Control Engineering Experimental Teaching Center, Lanzhou University of Technology, Lanzhou 730050, China
  • Received:2022-07-21 Online:2025-04-28 Published:2025-04-29

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摘要： 针对原始麻雀搜索算法全局搜索能力差、局部开发能力弱、易陷入局部最优等问题,提出一种基于自适应t分布的麻雀搜索算法(ATSSA).首先,通过Tent混沌映射初始化种群,增加初始种群的多样性;其次,利用自适应t分布变异算子对个体位置进行扰动,提高算法的全局搜索能力,同时结合动态选择概率来调节引入的t分布变异算子,平衡算法的全局搜索能力;最后,融合精英反向学习策略,在产生最优解的位置进行扰动,产生新解,促使算法跳出局部最优.仿真实验利用10个基准测试函数进行测试,结果表明ATSSA相较于SSA具有更好的寻优能力.将改进后的算法与深度极限学习机构建预测模型,选用辛烷值数据集进行实验,模型预测精度从87.31%提高到99.32%,验证了改进后的算法具有良好的工程应用前景.

关键词: 麻雀搜索算法, Tent混沌映射, 自适应t分布, 动态选择策略, 精英反向学习

Abstract: To solve the problems of the original sparrow search algorithm, such as poor global search ability, weak local development ability, and easy fall into local optimal, this paper proposes an adaptive t-distribution sparrow search algorithm (ATSSA) based on adaptive t-distribution. Tent chaos mapping is introduced to initialize the population to increase the diversity of the initial population. Subsequently, the adaptive t-distribution mutation operator is used to perturb individual positions to improve the global search ability. Meanwhile, the t-distribution mutation operator introduced by dynamic selection probability adjustment is used to balance the global search ability of the algorithm. Finally, the elite reverse learning strategy is integrated, and the location generated the optimal solution is disturbed to generate a new solution, which prompts the algorithm to jump out of the local optimal. Ten benchmark test functions are used in the simulation experiment, and the results show that ATSSA has better optimization ability than SSA. Moreover, ATSSA and deep extreme learning machines are used to construct a prediction model. The octane data set is used to carry out the experiments, and the prediction accuracy of the model is improved from 87.31% to 99.32%, verifying the algorithms promising potential for engineering applications.

Key words: sparrow search algorithm, Tent chaotic mapping, adaptive t distribution, dynamic selection strategy, elite reverse learning

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