计算机工程2025，Vol.51Issue(11)：72-79,8.DOI:10.19678/j.issn.1000-3428.0069820
基于斑马优化算法的通道自动剪枝方法
Automatic Channel Pruning Method Based on Zebra Optimization Algorithm
摘要
Abstract
The high computational and storage requirements of Convolutional Neural Networks(CNNs)limit their application in resource-limited mobile edge devices.Model compression techniques can significantly reduce the computational effort and parameters of CNNs without degrading network performance.Channel pruning has been proven to be effective for model compression.However,the pruning criteria of most existing channel pruning methods are based on assessing the importance of the channels or manually setting the evaluation criteria.The implementation of such methods requires the inclusion of more hyperparameters,and the pruning methods themselves lack automaticity.To address these limitations,a novel automatic channel-pruning method based on the Zebra Optimization Algorithm(ZOA)is proposed.This method begins with cluster pruning using k-medoids to form an initial compressed network structure,which is then utilized to iteratively optimize the network structure formed by the initial compression to search for the best compact network structure.Experimental results show that on the CIFAR-10 dataset,the Top-1 accuracy of this method improves by 0.24 percentage points over the baseline,while achieving Floating-Point Operations(FLOPs)and parameter pruning rates of 59.3％and 56.7％,respectively,on ResNet-56.关键词
通道剪枝/k-medoids聚类/迭代搜索/斑马优化算法/自动剪枝Key words
channel pruning/k-medoids clustering/iterative search/Zebra Optimization Algorithm(ZOA)/automatic pruning分类
信息技术与安全科学引用本文复制引用
刘亚军,仵大奎,范科峰,周文举..基于斑马优化算法的通道自动剪枝方法[J].计算机工程,2025,51(11):72-79,8.基金项目
国家重点研发计划(2021ZD0200406) （2021ZD0200406）
新一代人工智能国家科技重大专项(2021ZD0110600) （2021ZD0110600）
国家自然科学基金(61877065,61833011). （61877065,61833011）
