四川轻化工大学学报（自然科学版）2024，Vol.37Issue(2)：64-72,9.DOI:10.11863/j.suse.2024.02.09
基于个体记忆和高斯扰动的自适应灰狼算法
Adaptive Grey Wolf Optimization Algorithm Based on Individual Memory and Gaussian Perturbation
摘要
Abstract
Aiming at the problems of local optimization and slow convergence speed of traditional gray wolf algorithms in solving complex optimization problems,an adaptive gray wolf optimization algorithm(NGWO)based on individual memory and Gaussian perturbation has been proposed.Firstly,a nonlinear control parameter is introduced to balance the global exploration and local development abilities of the algorithm.Secondly,an adaptive position update formula with individual memory is proposed,which is used to accelerate the convergence speed and accuracy of the algorithm.Then,the ability of the algorithm to jump out of local optimum is strengthened by combining greedy algorithm and adaptive Gaussian perturbation.Through the test of the benchmark function,NGWO has a more accurate solution and a higher convergence speed compared with other optimization algorithms and improved algorithms.关键词
灰狼算法/非线性控制参数/自适应/高斯扰动/个体记忆Key words
grey wolf optimization/nonlinear control parameters/adaptive/Gaussian perturbation/individual memory分类
信息技术与安全科学引用本文复制引用
陈朗,陈昌忠,刘鑫..基于个体记忆和高斯扰动的自适应灰狼算法[J].四川轻化工大学学报（自然科学版）,2024,37(2):64-72,9.基金项目
国家自然科学基金项目(61902268) （61902268）
人工智能四川省重点实验室开放基金项目(2020RYJ05) （2020RYJ05）
