文章摘要
张啸宇,肖平,王淼,丁家乐.多策略结合改进麻雀搜索算法及其在电机控制的应用[J].井冈山大学自然版,2025,46(3):91-97
多策略结合改进麻雀搜索算法及其在电机控制的应用
IMPROVING SPARROW SEARCH ALGORITHM BY MULTI STRATEGY COMBINATION AND ITS APPLICATION IN MOTOR CONTROL
投稿时间:2024-10-21  修订日期:2024-12-11
DOI:10.3969/j.issn.1674-8085.2025.03.012
中文关键词: 麻雀搜索算法  PID控制  佳点集  反向学习策略  步长因子动态调整
英文关键词: sparrow search algorithm  PID controller  good point set  reverse learning strategy  dynamic adjustment of step size factor
基金项目:国家自然科学基金项目(52375227);安徽省重点研究与开发计划项目(2022A05020007);安徽省高校协同创新项目(GXXT-2023-039);芜湖市“赤铸之光”重大科技成果工程化项目(2023zc07)
作者单位E-mail
张啸宇 安徽工程大学机械与汽车工程学院, 安徽, 芜湖 241000
智能汽车线控底盘系统安徽省重点实验室, 安徽, 芜湖 241000 
 
肖平 安徽工程大学机械与汽车工程学院, 安徽, 芜湖 241000
智能汽车线控底盘系统安徽省重点实验室, 安徽, 芜湖 241000 
tlxp95@ahpu.edu.cn 
王淼 安徽工程大学电气工程学院, 安徽, 芜湖 241000  
丁家乐 安徽工程大学电气工程学院, 安徽, 芜湖 241000  
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中文摘要:
      针对麻雀搜索算法种群多样性较低、寻优精度不足、易陷入局部最优等问题,提出了一种多策略结合改进麻雀搜索算法。首先,在生成初始种群阶段,采用了反向学习策略和佳点集的方法,增加初始种群的多样性;然后,改进跟随者位置更新表达式,提升其寻优能力、增强寻优精度;之后,对步长因子动态调整,减少出现局部最优的概率。最终,通过对作用于四个测试函数图像进行比较,表明多策略结合改进麻雀搜索算法具有更好的优越性;并且使用该算法整定PID控制参数,以验证该算法的可行性与有效性。
英文摘要:
      To address the issues of low population diversity, insufficient optimization precision, and susceptibility to local optima in the sparrow search algorithm, a multi-strategy improved sparrow search algorithm is proposed. Firstly, during the initial population generation phase, a reverse learning strategy and the method of good point sets are employed to enhance the diversity of the initial population. Secondly, the position update expression for followers is improved to enhance their optimization capability and increase the precision of the search. Then, the step size factor is dynamically adjusted to reduce the likelihood of falling into local optima. Ultimately, by comparing the performance on four test functions, it is demonstrated that the multi-strategy improved sparrow search algorithm possesses superior advantages, furthermore, the algorithm is applied to tune the parameters of a PID controller for verifying its feasibility and effectiveness.
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