计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (7): 354-361.doi: 10.11896/jsjkx.230600181

• 人工智能 • 上一篇    下一篇

基于IMQ惯性权重策略的自适应灰狼优化算法

于明洋1, 李婷2,3, 许静1   

  1. 1 南开大学人工智能学院 天津 300350
    2 南开大学计算机学院 天津 300350
    3 天津津航技术物理研究所 天津 300350
  • 收稿日期:2023-06-24 修回日期:2023-11-06 出版日期:2024-07-15 发布日期:2024-07-10
  • 通讯作者: 李婷(t24725@126.com)
  • 作者简介:(2120220567@mail.nankai.edu.cn)
  • 基金资助:
    天津市自然科学基金(21JCYBJC00110)

Adaptive Grey Wolf Optimizer Based on IMQ Inertia Weight Strategy

YU Mingyang1, LI Ting2,3, XU Jing1   

  1. 1 College of Artificial Intelligence,Nankai University,Tianjin 300350,China
    2 College of Computer Science,Nankai University,Tianjin,300350,China
    3 Tianjin Jinhang Institute of Technical Physics,Tianjin,300350,China
  • Received:2023-06-24 Revised:2023-11-06 Online:2024-07-15 Published:2024-07-10
  • About author:YU Mingyang,born in 2000,postgra-duate.His main research interests include deep learning and intelligent optimization algorithm.
    LI Ting,born in 1988,master,senior engineer.Her main research interests include image big data governance,algorithm parity and intelligent recognition algorithm.
  • Supported by:
    Natural Science Foundation of Tianjin,China(21JCYBJC00110).

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摘要： 针对灰狼优化算法(Grey Wolf Optimizer,GWO)寻优精度低、收敛速度慢的问题,提出了一种基于IMQ惯性权重策略的自适应灰狼优化算法(ISGWO)。该算法利用IMQ函数的特性,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探能力和局部开发能力;同时,基于Sigmoid指数函数自适应更新个体位置,更好地搜索和优化问题的解空间。采用6个基本函数和29个CEC2017函数对ISGWO进行测试,并与6种常用的算法进行比较,实验结果表明ISGWO具有更优的收敛精度和速度。

关键词: IMQ函数, 惯性权重, 自适应, 灰狼优化算法, 收敛速度, 寻优精度

Abstract: Aiming at the problems of low optimization accuracy and slow convergence speed of grey wolf optimizer(GWO),this paper proposes an adaptive grey wolf optimization algorithm(ISGWO) based on IMQ inertia weighting strategy.This algorithm utilizes the properties of the IMQ function to achieve a nonlinear adjustment of the inertia weights,which better balances the global exploration ability and local exploitation ability of the algorithm.At the same time,it adaptively updates the position of individuals based on the Sigmoid exponential function to better search and optimize the solution space of the problem.Six basic functions and 29 CEC2017 functions are used to test ISGWO and compare it with six commonly used algorithms,and the experimental results show that ISGWO has superior convergence accuracy and speed.

Key words: IMQ function, Inertia weight, Adaptive, Grey wolf optimizer, Convergence speed, Optimization accuracy

中图分类号: 

  • TP301
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