基于三值多样性粒子群算法的MPRM电路综合优化

摘要

Based upon the research of discrete ternary particle swarm optimization,the ternary diversity particle swarm optimization (TDPSO) is proposed for optimization of mixed polarity Reed-Muller circuits (MPRM).According to the characteristics of mixed polarity XNOR/OR expression and probabilistic transition rules,motion equation of discrete ternary particle swarm optimization (DTPSO) is deduced.On the basis of DTPSO,a TDPSO algorithm is proposed by introducing extensive learning and ternary mutation.A mathematic mode for area and low power dissipation is built and mixed polarity conversion of XNOR/OR circuits is improved.Then on the basis of parameter mapping relations between particle and polarity,TDPSO is introduced to search the best polarity for power and area of MPRM circuits.Finally,the proposed algorithm is tested on ten circuits from MCNC benchmark circuits,and the experimental results show that the algorithm significantly outperforms the reported method.%通过对离散三值粒子群算法的研究,提出一种三值多样性粒子群算法以求解MPRM(Mixed-Polarity Reed-Muller,MPRM)电路综合优化问题.首先根据混合极性XNOR/OR展开式的特点和几率换算法则,推导出三值粒子群算法的运动方程,在此基础上,采用广泛学习策略和三值变异操作进行算法改进;然后建立三值多样性粒子群算法的粒子与MPRM电路极性的参数映射关系,结合估计模型和XNOR/OR电路混合极性转换方法,将所提算法应用于MPRM电路的最佳功耗和面积极性搜索;最后对10个PLA格式MCNC Benchmark电路进行测试.结果表明:与已发表的方法相比,该文的优化算法表现出了总体显著性的性能优势.