计算机工程与应用2025，Vol.61Issue(11)：204-215,12.DOI:10.3778/j.issn.1002-8331.2402-0221
聚类和群智能优化算法的自动剪枝方法
Automatic Channel Pruning Method Based on Clustering and Swarm Intelligence Optimization Algorithm
摘要
Abstract
In recent years,network pruning techniques have witnessed rapid development as highly effective solutions for compressing convolutional neural networks.Among them,channel pruning stands out due to its hardware-friendly nature.Current mainstream methods focus on setting manual constraints as evaluation criteria,which are inefficient and can easily lead to suboptimal results.On the other hand,existing automatic pruning methods based on search algorithms struggle to strike a balance between search space and search efficiency.To address these issues,a novel automatic channel pruning method based on clustering and swarm intelligence optimization algorithms is proposed.Specifically,the method employs the K-Medoids algorithm to perform layer-wise channel clustering based on the similarity of feature maps.Through sen-sitivity analysis,the current optimal pruning rate is determined,forming an initial compressed model that simplifies the search space for swarm intelligence optimization algorithms.The particle swarm optimization(PSO)algorithm is intro-duced to iteratively search and optimize the pruned structure of the initial compression model using meta-learning,resulting in the identification of the optimal pruned network structure.Finally,the pruned network is fine-tuned to mitigate any accuracy loss caused by pruning.Evaluation experiments are conducted on CIFAR-10 and ILSVRC-2012 datasets using several commonly used CNN models.Comparative results with mainstream methods from recent years demonstrate improvements,thereby validating the effectiveness of the pruned networks.For instance,on the ILSVRC-2012 dataset,with a pruning rate of 45.5%having been achieved on ResNet-50,the model's accuracy has only dropped by 0.23 percent-age points.关键词
卷积神经网络/模型压缩/网络剪枝/网络结构搜索/粒子群算法Key words
convolutional neural network(CNN)/model compression/network pruning/neural architecture search(NAS)/particle swarm optimization(PSO)分类
信息技术与安全科学引用本文复制引用
刘洲峰,吴文涛,李环宇,邵昕楠,李春雷..聚类和群智能优化算法的自动剪枝方法[J].计算机工程与应用,2025,61(11):204-215,12.基金项目
国家自然科学基金(62072489) （62072489）
中原科技创新领军人才项目(234200510009) （234200510009）
河南省科技攻关项目(222102210008). （222102210008）
