Integrated Polarity Optimization of MPRM Circuits Based on Improved Multi-objective Particle Swarm Optimization

FU Qiang; WANG Pengjun; TONG Nan; WANG Mingbo; ZHONG Caiming

doi:10.1049/cje.2020.07.005

Volume 29 Issue 5

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Article Navigation > Chinese Journal of Electronics > 2020 > 29(5): 833-840

FU Qiang, WANG Pengjun, TONG Nan, et al., “Integrated Polarity Optimization of MPRM Circuits Based on Improved Multi-objective Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 29, no. 5, pp. 833-840, 2020, doi: 10.1049/cje.2020.07.005

Citation:

FU Qiang, WANG Pengjun, TONG Nan, et al., “Integrated Polarity Optimization of MPRM Circuits Based on Improved Multi-objective Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 29, no. 5, pp. 833-840, 2020, doi: 10.1049/cje.2020.07.005

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Integrated Polarity Optimization of MPRM Circuits Based on Improved Multi-objective Particle Swarm Optimization

doi: 10.1049/cje.2020.07.005

1.
College of Science and Technology, Ningbo University, Ningbo 315300, China;
2.
College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China;
3.
Faculty of Electronic Engineering and Computer Science, Ningbo University, Ningbo 315211, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61874078, No.61875098), and the K. C. Wong Magna Fund in Ningbo University.

More Information

Corresponding author: WANG Pengjun (corresponding author) was born in 1966. He received the Ph.D. degree in electronic engineering from East China University of Chemical Technology. He is now a professor of Wenzhou University, a senior member of Chinese Institute of Electronics, senior member of Chinese Computer Federation, member of Electronic Circuits and Systems Professional Committee of Chinese Institute of Electronics. His research interests include multi-valued logic circuits and low power integrated circuit design theory. (Email:wangpengjun@wzu.edu.cn)
Received Date: 2018-10-17
Rev Recd Date: 2020-06-24
Publish Date: 2020-09-10

Abstract

Abstract

Aiming at the multi-objective polarity design of Mixed-polarity Reed-Muller (MPRM) circuit, such as small area and low power consumption, an integrated polarity optimization scheme based on improved Multi-objective particle swarm optimization (MOPSO) is proposed. In the Improved MOPSO (IMOPSO) algorithm, particles in the external archive can be actively evolved through self-learning operations to find better circuit polarity. The particles in the population achieve selflearning fractals by comparing the differences between their own states and individuals in external archive to enhance the evolutionary level of the population. A multiobjective decision model of area and power consumption is established according to the characteristics of MPRM circuit. The tabular technique and the IMPOPO algorithm are combined to obtain the Pareto optimal polarity set of the MPRM circuit for area and power consumption. The MCNC Benchmark circuit is used to test the performance of the algorithm. The results verify the effectiveness of the proposed algorithm.
- Multi-objective particle swarm optimization (MOPSO),
- Self-learning fractal,
- MPRM circuits,
- Polarity,
- Area-power trade-off

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References(16)

References

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