计算机与数字工程2025，Vol.53Issue(4)：984-988,1050,6.DOI:10.3969/j.issn.1672-9722.2025.04.012
多策略融合的改进海洋捕食者算法及其应用
Modified Marine Predator Algorithm Based on Multi-strategy Fusion and its Application
摘要
Abstract
In order to solve the problem of local fans adjusting wind speed in advance according to the next time demand vol-ume,a novel Elman neural network algorithm based on modified marine predator algorithm(MMPA)is proposed to predict the de-mand volume.Firstly,the chaotic map is used to initialize the population to improve the inhomogeneity of the population position,reverse learning is introduced to operate the individuals before each iteration,differential operation is introduced to the prey matrix in the middle and late iteration,and several test functions are selected to test it.Secondly,the improved marine predator algorithm is used to optimize the initial weights and thresholds in the Elman neural network to improve the accuracy of the required air volume prediction results.The results show that the prediction accuracy of MMPA-Elman neural network model is higher,and the accurate prediction of air volume is realized,which provides guarantee for the safety production of coal mines.关键词
海洋捕食者算法/Elman神经网络/需风量预测Key words
marine predators algorithm/Elman neural network/air demand prediction分类
矿业与冶金引用本文复制引用
倪云峰,张定坤,王静,郭苹..多策略融合的改进海洋捕食者算法及其应用[J].计算机与数字工程,2025,53(4):984-988,1050,6.基金项目
国家自然科学基金项目(编号:61701393) （编号:61701393）
陕西省教育厅科学研究计划项目(编号:19JK0528,19JK0531)资助. （编号:19JK0528,19JK0531）
