An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies

HE Juncai; HE Zhenxue; LIU Jia; ZHANG Yan; ZHANG Fan; LIANG Fangfang; WANG Tao; XIAO Limin; WANG Xiang

doi:10.23919/cje.2022.00.358

Volume 33 Issue 2

Mar. 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(2): 423-435

Juncai HE, Zhenxue HE, Jia LIU, et al., “An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies,” Chinese Journal of Electronics, vol. 33, no. 2, pp. 423–435, 2024 doi: 10.23919/cje.2022.00.358

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Juncai HE, Zhenxue HE, Jia LIU, et al., “An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies,” Chinese Journal of Electronics, vol. 33, no. 2, pp. 423–435, 2024 doi: 10.23919/cje.2022.00.358

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An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies

doi: 10.23919/cje.2022.00.358

HE Juncai^1
,,
HE Zhenxue^{1
,
,},
LIU Jia^1
,,
ZHANG Yan^1
,,
ZHANG Fan^1
,,
LIANG Fangfang^1
,,
WANG Tao^2
,,
XIAO Limin^3
,,
WANG Xiang^4
,

1.
Hebei Agricultural University, Baoding 071001, China
2.
Beijing Information Science and Technology University, Beijing 100192, China
3.
School of Computer Science and Engineering, Beihang University, Beijing 100091, China
4.
School of Electronic and Information Engineering, Beihang University, Beijing 100091, China

More Information

Author Bio:
Juncai HE is a M.S. candidate in the School of Information Science and Technology, Hebei Agricultural University, China. His research interests include integrated circuit design and swarm intelligent optimization algorithm. (Email: 20202060094@pgs.hebau.edu.cn)

Zhenxue HE received the Ph.D. degree in computer architecture from Beihang University, Beijing, China, in 2018. He is currently a Full Associate Professor with Hebei Agricultural University. He has authored or coauthored more than 30 articles in peer-reviewed journals and proceedings, such as IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, IEEE Transactions on Services Computing, IEEE Sensors Journal, and International Journal of Intelligent Systems. His research interests include low power integrated circuits design and optimization, multiple-valued logic circuits, combinatorial optimization, and intelligent algorithm. (Email: hezhenxue@buaa.edu.cn)

Jia LIU received the B.S. degree from the School of Accounting, Hebei Finance University, Baoding, China, in 2011, and received the M.S. degree from the School of Management, Hebei University, Baoding, China, in 2019. She is currently a staff in Hebei Agricultural University. Her research interests include electronics design automation, intelligent algorithm, finance data mining, and data analysis. (Email: hzx@hebau.edu.cn)

Yan ZHANG received the M.S. degree from the School of Information Science and Technology of Hebei Agricultural University. Her research interests include electronics design automation and intelligent algorithm. (Email: 278643416@qq.com)

Fan ZHANG received the Ph.D. degree in engineering from Hebei Agricultural University, Baoding, China, in 2012. She is currently an Associate Professor in the School of Information Science and Technology at Hebei Agricultural University. Her research interests include electronics design automation and intelligent algorithm. (Email: ellenzhang0911@126.com)

Fangfang LIANG received the Ph.D. degree in engineering from Beijing University of Technology, Beijing, China, in 2020. She is currently an Associate Professor in the School of Information Science and Technology at Hebei Agricultural University. Her research interests include electronics design automation and intelligent algorithm. (Email: liangfangfang@hebau.edu.cn)

Tao WANG received the Ph.D. degree in engineering from Beihang University, Beijing, China. He is currently a Full Associate Professor with Beijing Information Science and Technology University. His research focuses on intelligent optimization. (Email: wt860122@buaa.edu.cn)

Limin XIAO received the B.S. degree in computer science (major) and physics (minor) from the Department of Computer Science, Tsinghua University, Beijing, China, in 1993, and the M.S. and Ph.D. degrees in computer science from the Institute of Computer Science, Chinese Academy of Sciences, Beijing, in 1996 and 1998, respectively. He is currently a Professor with the School of Computer Science and Engineering, Beihang University, China. He is a senior member of the Chinese Computer Society, and member of the Cloud Computing Expert Committee of the Chinese Institute of Electronics. His main research areas include computer architecture, computer system software, high-performance computing, virtualization, and cloud computing. (Email: 930111386@qq.com)

Xiang WANG received the Ph.D. degree in engineering from the School of Information Technology, Peking University, Beijing, China, in 2004. He is currently a Professor with the School of Electronic and Information Engineering, Beihang University, China. He is currently a TPC member of the IEEE Conference Organizing Committee, Vice Chairman of the Conference, and Chairman of the Conference. His main research areas include very large-scale integration, micro-nano systems, genetic circuits, and aerospace information networks. (Email: wxiang@buaa.edu.cn)
Corresponding author: Email: hezhenxue@buaa.edu.cn
Received Date: 2022-10-19
Accepted Date: 2023-02-14

Available Online: 2023-07-12

Publish Date: 2024-03-05

Abstract

Abstract

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller (RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM (MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy (TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach (TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina (MCNC) validate the effectiveness and superiority of the proposed TMMPOA.
- Multi-output mixed polarity Reed-Muller,
- Power optimization,
- Combinatorial optimization problem,
- Whale optimization algorithm,
- Two-populations strategy,
- Mutation strategy

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References(33)

References

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