Bayesian-based ant colony optimization algorithm for edge detection

doi:10.23919/JSEE.2025.000083

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (4): 892-902.doi: 10.23919/JSEE.2025.000083

• ELECTRONICS TECHNOLOGY • Previous Articles

Bayesian-based ant colony optimization algorithm for edge detection

Yongbin YU(), Yuanjingyang ZHONG(), Xiao FENG(), Xiangxiang WANG(), Ekong FAVOUR(), Chen ZHOU(), Man CHENG(), Hao WANG(), Jingya WANG()

School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Received:2023-05-10 Accepted:2023-11-22 Online:2025-08-18 Published:2025-09-04
Contact: Yuanjingyang ZHONG E-mail:ybyu@uestc.edu.cn;202022090627@std.uestc.edu.cn;fengxiaocd@gmail.com;xxwang@uestc.edu.cn;favourekong127@yahoo.com;zhouchen090616@163.com;2564778062@qq.com;wh_chengdu@126.com;wjycindy@163.com
About author:
YU Yongbin was born in 1975. He received his Ph.D. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2008. He is currently an associate professor in the School of Information and Software Engineering, UESTC. His research interests include big data and memristor. E-mail: ybyu@uestc.edu.cn

ZHONG Yuanjingyang was born in 1997. She received her B.S. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2020. She is currently pursuing her M.S. degree with the School of Information and Software Engineering, UESTC. Her research interests include swarm intelligence and field programmable gate array. E-mail: 202022090627@std.uestc.edu.cn

FENG Xiao was born in 1993. He received his B.E. degree in electric engineering and automation from Shanghai University of Electric Power, Shanghai, China, in 2016, and M.E. degree in integrated design engineering from Keio University, Yokohama, Japan, in 2020. He is currently pursuing his Ph.D. degree with the University of Electronic Science and Technology of China (UESTC), China. His research interests include evolutionary algorithm, neural network, and control system. E-mail: fengxiaocd@gmail.com

WANG Xiangxiang was born in 1992. He received his Ph.D. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2023. From June 2021 to June 2022. He is a lecturer in the School of Information and Software Engineering, UESTC. His current research interests include memristive neural networks, complex neural networks, impulsive control, natural language processing and synchronization analysis. E-mail: xxwang@uestc.edu.cn

FAVOUR Ekong was born in 1998. He received his B.S. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2019. He also received his M.S. degree from the School of Information and Software Engineering, University of Electronic Science and Technology of China, in 2021. His research interests include deep learning, medical imaging, computer vision, and artificial intelligence. E-mail: favourekong127@yahoo.com

ZHOU Chen was born in 1998. She received her B.S. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2020. She is currently pursuing her M.S. degree with the School of Information and Software Engineering, UESTC. Her research interests include memristor and genetic algorithm. E-mail: zhouchen090616@163.com

CHENG Man was born in 1999. She received her B.S degree from the University of Electronic Science and Technology of China (UESTC), in 2016. She is currently pursuing her M.S. degree with the School of Information and Software Engineering, UESTC. Her research interest is image captioning. E-mail: 2564778062@qq.com

WANG Hao was born in 1997. He received his B.E. degree from Southwest University for Nationalities, in 2020, and M.E. degree with the School of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), in 2023. His research interests include natural language processing and medical image processing. E-mail: wh_chengdu@126.com

WANG Jingya was born in 1998. She received her B.S. degree in computer science and technology from Shandong University of Finance and Economics, in 2020. She is currently pursuing her Ph.D. degree in software engineering with University of Electronic Science and Technology of China. Her research interests include deep learning, neural network control, and dynamic analysis. E-mail: wjycindy@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62276055).

Abstract

Abstract:

Ant colony optimization (ACO) is a random search algorithm based on probability calculation. However, the uninformed search strategy has a slow convergence speed. The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process, reducing the uncertainty in the random search process. Due to the ability of the Bayesian algorithm to reduce uncertainty, a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection. In addition, this paper has the following two innovations on the basis of the classical algorithm, one of which is to add random perturbations after completing the pheromone update. The second is the use of adaptive pheromone heuristics. Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm, due to the improvement of the pheromone utilization rate. Moreover, Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.

Key words: ant colony optimization (ACO), Bayesian algorithm, edge detection, transfer function

Yongbin YU, Yuanjingyang ZHONG, Xiao FENG, Xiangxiang WANG, Ekong FAVOUR, Chen ZHOU, Man CHENG, Hao WANG, Jingya WANG. Bayesian-based ant colony optimization algorithm for edge detection[J]. Journal of Systems Engineering and Electronics, 2025, 36(4): 892-902.

Figures/Tables 13

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ID	Roberts	Sobel	LOG	Canny	Trad	Bayes
24063	48.65	45.07	55.22	59.36	87.92	88.84
365072	36.99	40.87	51.65	57.71	80.45	81.66
227092	51.69	50.58	58.72	60.94	91.06	91.28

ID	Roberts	Sobel	LOG	Canny	Trad	Bayes
24063	55.86	49.21	58.56	60.94	90.93	91.52
365072	39.77	42.99	52.51	58.57	82.40	83.71
227092	55.48	51.60	61.29	60.97	93.38	93.52

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Bayesian-based ant colony optimization algorithm for edge detection

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