Memristive network-based genetic algorithm and its application to image edge detection

doi:10.23919/JSEE.2021.000091

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (5): 1062-1070.doi: 10.23919/JSEE.2021.000091

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Memristive network-based genetic algorithm and its application to image edge detection

Yongbin YU^1,*(), Chenyu YANG¹(), Quanxin DENG¹(), Tashi NYIMA²(), Shouyi LIANG³(), Chen ZHOU¹()

¹ School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
² School of Information Science and Technology, Tibet University, Lhasa 850000, China
³ Department of Economics, Stony Brook University, New York 11794, U.S.

Received:2021-01-21 Online:2021-10-18 Published:2021-11-04
Contact: Yongbin YU E-mail:ybyu@uestc.edu.cn;chenyu_yang_divine@163.com;2507120731@qq.com;nmzx@tibet.edu.cn;15008231993@163.com;756349535@qq.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 at the School of Information and Software Engineering, UESTC. His research interests include big data and memristor. E-mail: ybyu@uestc.edu.cn||YANG Chenyu was born in 1994. He received his B.S. and M.S. degrees from the UESTC, Chengdu, in 2016 and 2019 respectively. His research interests include memristor and genetic algorithm. E-mail: chenyu_yang_divine@163.com||DENG Quanxin was born in 1996. He received his B.S. degree from the UESTC, Chengdu, in 2018. He is currently pursuing his M.S. degree with the School of Information and Software Engineering, UESTC. His research interests include memristor and genetic algorithm. E-mail: 2507120731@qq.com||NYIMA Tashi was born in 1964. He received his Ph.D. degree from Sichuan University, Chengdu, in 2009. He is currently a professor in Tibet University. His research interests include computer network and information system. E-mail: nmzx@tibet.edu.cn||LIANG Shouyi was born in 1993. He received his B.S. degree from Sichuan University, Chengdu, in 2016. He received his M.A. degree from the Stony Brook University, New York, in 2020. His research interests include memristor and econometrics. E-mail: 15008231993@163.com||ZHOU Chen was born in 1998. She received her B.S. degree from the UESTC, Chengdu, in 2020. She is currently pursuing her M.S. degree with the School of Information and Software Engineering, UESTC. His research interests include memristor and genetic algorithm. E-mail: 756349535@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61550110248), the Sichuan Science and Technology Department project (2019YFG0190), and the University of Electronic Science and Technology of China project (H04W170186)

Abstract

Abstract:

This paper proposes a mem-computing model of memristive network-based genetic algorithm (MNGA) by building up the relationship between the memristive network (MN) and the genetic algorithm (GA), and a new edge detection algorithm where image pixels are defined as individuals of population. First, the computing model of MNGA is designed to perform mem-computing, which brings new possibility of the hardware implementation of GA. Secondly, MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed. Finally, simulation results demonstrate that the figure of merit (FoM) of our model is better than the latest memristor-based swarm intelligence. In summary, a new way is found to build proper matching of memristor to GA and aid image edge detection.

Key words: memristive network (MN), genetic algorithm (GA), edge detection, mem-computing

Yongbin YU, Chenyu YANG, Quanxin DENG, Tashi NYIMA, Shouyi LIANG, Chen ZHOU. Memristive network-based genetic algorithm and its application to image edge detection[J]. Journal of Systems Engineering and Electronics, 2021, 32(5): 1062-1070.

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References 33

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Method	Algorithm	Application
[11]	ACO	TSP
[10]	ACO	Image edge detection
The proposed method	GA	Image edge detection

Symbol	Meaning
$ G $	Generations
$ P $	Population
$ B $	Individual
$ C $	Chromosome
$ gl $	Gene list
$ {S}_{r}\left(b\right) $	Rule of selection
$ {C}_{r}({b}_{1},{b}_{2}) $	Rule of crossover
$ {B}_{r}\left(b\right) $	Rule of biomutation
$ FF\left(b\right) $	Fitness function

Nature	GA	Mem-computing
Population	Some feasible solutions	Memristive network
Individual	Feasible solution	Memristive subnet
Chromosome	Part of the feasible solution	Memristor
Gene	Feasible solution coding	Memristance
Natural selection	Solution selection	Memristor selection
Biomutation	Solution-coding change	Memristance change
Crossover	Addition of individual	Addition of memristor

Symbol	Value	Meaning
$ {R}_{c} $	$ 0.4 $	Rate of crossover
$ {R}_{b} $	$ 0.05 $	Rate of biomutation
$ {F}_{m} $	Mean value	Threshold value of fitness
$ w \cdot h $	$ 468\times 468 $	Resolution of image
$ MN\left(x,y\right) $	$0\;{\rm{or}}\;1$	Memristance
$ P(x,y) $	$0- 255$	Pixel value
$ F(x,y) $	Calculated by fitness function	Fitness of $ P(x,y) $ and $ MN(x,y) $
$ {C}_{c} $	$ 4 $	The neighborhood size of crossover processing
$ {\rm{Mean}} $	$ 33.667\;8 $	Mean value of fitness

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Memristive network-based genetic algorithm and its application to image edge detection

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