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

作　　者：FU Qiang WANG Pengjun TONG Nan WANG Mingbo ZHONG Caiming 

机构地区：[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

出　　处：《Chinese Journal of Electronics》2020年第5期833-840,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61874078,No.61875098).

摘　　要：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 multiobjec tive 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 Pare to 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.

关 键 词：Multi-objective particle swarm optimization(MOPSO) Self-learning fractal MPRM circuits Polarity Area-power trade-off. 

分 类 号：TN431.2[电子电信—微电子学与固体电子学] TP18[自动化与计算机技术—控制理论与控制工程]