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A power optimization approach for Mixed Polarity Reed_Muller logic circuits based on multi-strategy fusion memetic algorithm
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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, 1998, -1(-1): .
@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",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="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 Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 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 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 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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