基于MODPSO算法的FPRM电路多约束极性优化方法

摘要

为求解较大规模FPRM逻辑电路中多约束条件下的极性优化问题,该文提出一种基于多目标离散粒子群允化(Multi-Objective Discrete Particle Swarm Optimization,MODPSO)算法的求解方法.首先针对FPRM电路极性设计需要满足延时短、面积小的多约束要求,构建了多目标决策模型.然后结合极性转换算法和MODPSO算法,对电路进行最优极性搜索,以获取电路延时和面积的Pareto最优解集.最后利用17个MCNC Benchmark电路进行测试,并将MODPSO算法与DPSO算法、NSGA-II算法进行实验对比,结果验证了算法的有效性.%For multi-constrained polarity optimization of large-scale FPRM circuits,a Multi-Objective Discrete Particle Swarm Optimization (MODPSO) algorithm is proposed.Firstly,the multi-objective decision model is established according to the delay-area trade-off of large-scale FPRM circuits.Secondly,combined with tabular technique and MODPSO,the best polarities of delay and area are searched for large-scale FPRM circuits,to obtain the Pareto optimal set for delay and area.Finally,the algorithm MODPSO is compared with the algorithm DPSO and NSGA-II on MCNC Benchmarks with PLA format,and the results verify the effectiveness of the MODPSO.