CLC number: TP181
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-04-20
Cited: 0
Clicked: 1957
Citations: Bibtex RefMan EndNote GB/T7714
Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU. A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1557-1573.
@article{title="A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search",
author="Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="11",
pages="1557-1573",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200515"
}
%0 Journal Article
%T A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search
%A Han YAN
%A Chongquan ZHONG
%A Yuhu WU
%A Liyong ZHANG
%A Wei LU
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 11
%P 1557-1573
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200515
TY - JOUR
T1 - A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search
A1 - Han YAN
A1 - Chongquan ZHONG
A1 - Yuhu WU
A1 - Liyong ZHANG
A1 - Wei LU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 11
SP - 1557
EP - 1573
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200515
Abstract: convolutional neural networks (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of mixed-variable encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the gaussian process and particle swarm optimization (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter mixed-variable problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on imageclassification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.
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