Affiliation(s):
Intelligent Agricultural Equipment Research Institute, Hebei Agricultural University, Baoding 071001, China;
moreAffiliation(s): Intelligent Agricultural Equipment Research Institute, Hebei Agricultural University, Baoding 071001, China; Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China; School of Computer Science and Engineering, Beihang University, Beijing 100191, China; School of Electronic Information Engineering, Beihang University, Beijing 100191, China;
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Mengyu ZHANG, Zhenxue HE, Yijin WANG, Xiaojun ZHAO, Xiaodan ZHANG, Limin XIAO, Xiang WANG. A power optimization approach for Mixed Polarity Reed_Muller logic circuits based on multi-strategy fusion memetic algorithm[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400513
@article{title="A power optimization approach for Mixed Polarity Reed_Muller logic circuits based on multi-strategy fusion memetic algorithm", author="Mengyu ZHANG, Zhenxue HE, Yijin WANG, Xiaojun ZHAO, Xiaodan ZHANG, Limin XIAO, Xiang WANG", journal="Frontiers of Information Technology & Electronic Engineering", year="in press", publisher="Zhejiang University Press & Springer", doi="https://doi.org/10.1631/FITEE.2400513" }
%0 Journal Article %T A power optimization approach for Mixed Polarity Reed_Muller logic circuits based on multi-strategy fusion memetic algorithm %A Mengyu ZHANG %A Zhenxue HE %A Yijin WANG %A Xiaojun ZHAO %A Xiaodan ZHANG %A Limin XIAO %A Xiang WANG %J Frontiers of Information Technology & Electronic Engineering %P %@ 2095-9184 %D in press %I Zhejiang University Press & Springer doi="https://doi.org/10.1631/FITEE.2400513"
TY - JOUR T1 - A power optimization approach for Mixed Polarity Reed_Muller logic circuits based on multi-strategy fusion memetic algorithm A1 - Mengyu ZHANG A1 - Zhenxue HE A1 - Yijin WANG A1 - Xiaojun ZHAO A1 - Xiaodan ZHANG A1 - Limin XIAO A1 - Xiang WANG J0 - Frontiers of Information Technology & Electronic Engineering SP - EP - %@ 2095-9184 Y1 - in press PB - Zhejiang University Press & Springer ER - doi="https://doi.org/10.1631/FITEE.2400513"
Abstract: The power optimization of Mixed Polarity Reed-Muller (MPRM) logic circuits is a classic combinatorial optimization problem. Existing approaches often suffer from slow convergence and a propensity to converge to local optima, limiting their effectiveness in achieving optimal power efficiency. Firstly, we propose a novel Multi-strategy Fusion Memetic Algorithm (MFMA). MFMA integrates global exploration via the Chimp Optimization Algorithm with local exploration using the Coati Optimization Algorithm based on the Optimal position Learning and Adaptive weight factor, complemented by population management through truncated selection. Leveraging MFMA, we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit power. Experimental results based on Microelectronics Center of North Carolina (MCNC) benchmark circuits demonstrate significant improvements over existing power optimization approaches. MFMA achieves a maximum power saving rate of 72.30% and an average optimization rate of 36.21%; the search solutions are faster and of higher quality, validating the effectiveness and superiority of MFMA.
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