计算机应用研究2024，Vol.41Issue(6)：1656-1662,7.DOI:10.19734/j.issn.1001-3695.2023.10.0518
基于自适应交叉与协方差学习的改进平衡优化器算法
Improved equilibrium optimizer based on adaptive crossover and covariance learning
摘要
Abstract
Aiming at the problems of low convergence accuracy and ease of trapping into local stagnation in the equilibrium optimizer,this paper proposed an improved equilibrium optimizer based on adaptive crossover and covariance learning.First-ly,this algorithm constructed an external archive to retain the historically dominant individuals and increase the population di-versity for improving the global optimization ability.Secondly,it introduced an adaptive crossover probability to balance the global exploration ability and local exploitation ability of the algorithm,so as to improve the optimization accuracy and robust-ness of the algorithm.Finally,it applied a covariance learning strategy to make full use of the relationship between the concen-tration vectors to enhance the information exchange among the populations and thereby to avoid local stagnation.Through simu-lation experiments on the CEC2019 test functions and combining the improved algorithm with back propagation(BP)neural network to predict the runoff situation of the Manas River in Xinjiang,the experimental results show that the improved algo-rithm remarkably improves convergence accuracy and robustness,and significantly enhances the runoff prediction performance of the BP neural network.关键词
平衡优化器算法/智能算法/外部存档/自适应交叉概率/协方差/径流预测Key words
equilibrium optimizer/intelligence algorithm/external archive/adaptive crossover rate/covariance/runoff prediction分类
信息技术与安全科学引用本文复制引用
侯新宇,鲁海燕,卢梦蝶,胡清元..基于自适应交叉与协方差学习的改进平衡优化器算法[J].计算机应用研究,2024,41(6):1656-1662,7.基金项目
国家自然科学基金资助项目(12102146) （12102146）
