非线性参数的精英学习灰狼优化算法

doi:10.16088/j.issn.1001-6600.2020093002

摘要/Abstract

摘要： 为了有效提高灰狼优化算法的收敛速度及求解精度,本文结合精英反向学习策略增加种群的多样性,将收敛因子从线性变为非线性,重新设计位置更新公式提高算法的收敛精度,提出一种非线性参数的精英学习灰狼优化算法。8组典型测试函数的实验测试表明,算法的收敛速度和收敛精度均有不同程度的提高。在求解IIR数字滤波器优化设计问题时,表现出了优良的性能。

关键词: 灰狼优化算法, 精英反向学习, 非线性参数, 测试函数, IIR数字滤波器设计

Abstract: In order to effectively improve the convergence speed and solution accuracy of grey wolf optimization algorithm, this paper combines the elite reverse learning strategy to increase the diversity of population, changes the convergence factor from linear to nonlinear, redesigns the position update formula to improve the convergence accuracy of the algorithm, and proposes an elite learning gray wolf optimization algorithm with nonlinear parameters. The experimental results of 8 groups of typical test functions show that the convergence speed and accuracy of the algorithm are improved to different degrees. When solving the optimization design problem of IIR digital filter, it shows excellent performance.

Key words: gray wolf optimization algorithm, elite opposition-based learning, nonlinear parameters, test function, IIR digital filter design

中图分类号:

TP301.6

逯苗, 何登旭, 曲良东. 非线性参数的精英学习灰狼优化算法[J]. 广西师范大学学报（自然科学版）, 2021, 39(4): 55-67.

LU Miao, HE Dengxu, QU Liangdong. Grey Wolf Optimization Algorithm Based on Elite Learning for Nonlinear Parameters[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 55-67.

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