Evolving convolution neural networks using particle swarm optimization for image classification

Zhenpeng Wang; Jixiang Cheng; Dan Wu; HongBin He; Shu Xiao

doi:10.1117/12.2634388

4 May 2022 Evolving convolution neural networks using particle swarm optimization for image classification

Zhenpeng Wang, Jixiang Cheng, Dan Wu, HongBin He, Shu Xiao

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217223 (2022) https://doi.org/10.1117/12.2634388
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

The design of an efficient and compact convolutional neural network (CNN) for image classification is a very challenging problem, and its design process relies heavily on the experience of experts and continuous trial and error. This paper proposed a search method based on variable scale convolutional neural network by comprehensively considering the convolutional layer, pooling layer, fully-connection layer, and activation function in CNN. In this method, a search space is designed, and the categories of search parameters are maximized. At the same time, a particle swarm variable length coding mapping method is proposed to solve the problem of coding redundancy of candidate networks. In order to effectively evaluate the performance of candidate networks, the evaluation method of the random dataset is adopted to reduce the evaluation time of CNN and improve the stability of training. The proposed search model is compared with many empirical design models and other search methods in four image classification tasks. The experimental results show that the proposed method has strong advantages and competitiveness in classification accuracy and model size compared with the existing methods.

Citation Download Citation

Zhenpeng Wang, Jixiang Cheng, Dan Wu, HongBin He, and Shu Xiao "Evolving convolution neural networks using particle swarm optimization for image classification", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217223 (4 May 2022); https://doi.org/10.1117/12.2634388

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KEYWORDS

Convolution

Particles

Neural networks

Convolutional neural networks

Image classification

Neurons

Evolutionary algorithms

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