自适应t分布与黄金正弦改进的麻雀搜索算法及其应用

张伟康; 刘升

doi:10.19304/J.ISSN1000-7180.2021.0026

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自适应t分布与黄金正弦改进的麻雀搜索算法及其应用

人工智能与算法 | 更新时间：2026-09-04

- 自适应t分布与黄金正弦改进的麻雀搜索算法及其应用
- Improved sparrow search algorithm based on adaptive t-distribution and golden sine and its application
- 微电子学与计算机 2022年39卷第3期页码：17-24
- 作者机构：
  
  上海工程技术大学机构管理学院,上海,201620
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61075115国家自然科学基金 61673258上海市自然科学基金 19ZR1421600
- DOI：10.19304/J.ISSN1000-7180.2021.0026
  中图分类号：
- 纸质出版：2022
- 稿件说明：
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张伟康, 刘升. 自适应t分布与黄金正弦改进的麻雀搜索算法及其应用[J]. 微电子学与计算机, 2022, 39(3): 17-24.

ZHANG Weikang, LIU Sheng. Improved sparrow search algorithm based on adaptive t-distribution and golden sine and its application[J]. 2022, 39(3): 17-24.
张伟康, 刘升. 自适应t分布与黄金正弦改进的麻雀搜索算法及其应用[J]. 微电子学与计算机, 2022, 39(3): 17-24. DOI： 10.19304/J.ISSN1000-7180.2021.0026.

ZHANG Weikang, LIU Sheng. Improved sparrow search algorithm based on adaptive t-distribution and golden sine and its application[J]. 2022, 39(3): 17-24. DOI： 10.19304/J.ISSN1000-7180.2021.0026.

摘要

针对麻雀搜索算法存在的容易陷入局部最优、收敛速度慢等问题，提出一种基于自适应t分布与黄金正弦改进的麻雀搜索算法(t-GSSA).首先，通过黄金正弦算法改进发现者的位置更新方式，增强算法局部开发和全局探索能力，并且提高算法的收敛能力；其次，利用自适应t分布变异方法，对个体位置进行扰动，提升算法跳出局部最优的能力；然后，在仿真实验中与4种基本算法和3种改进算法基于10个基准测试函数进行比较，结果表明所提算法具有更好的收敛性和求解精度；最后，将t-GSSA算法应用到比例-积分-微分(PID)参数整定中.仿真结果表明，所提算法优化后的控制器具有更快的响应速度的更稳定的精度.

Abstract

Aiming at the shortcomings such as easy to fall into local optimization and low convergence

an improved Sparrow Search Algorithm based on adaptive t-distribution and gold sine improvement is proposed. Firstly

the golden sine algorithm is used to improve the location update mode of the finder

enhance the local development and global exploration ability of the algorithm

and improve the convergence ability of the algorithm. Secondly

the t-GSSA algorithm is used to disturb the individual position to improve the algorithms ability to jump out of local optimality. And then

compared with 4 basic algorithms and 3 improved algorithms based on 10 benchmark functions

the simulationresults show that the proposed algorithm has better convergence and solution accuracy. Finally

the t-GSSA algorithm is applied to the proportional-integral-score (PID) parameter integration. The simulation results show that the controller that optimized by the proposed algorithm has more stable accuracy with faster response speed.

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references

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