基于 SADPSO 的 MPRM 最小化算法

针对混合极性Reed-Muller(mixed-polarity Reed-Muller,MPRM)逻辑最小化问题,提出一种基于SADPSO (hybrid simulated annealing and discrete particle swarm optimization)的智能算法.该算法将模拟退火(simulated anhealing,SA)与离散粒子群优化(discrete particle swarm optimization,DPSO)相结合,对DPSO所得到的最佳解应用SA,帮助算法跳出局部极小.使用所提出算法和已有智能MPRM最小化算法分别对23个MCNC基准电路进行逻辑最小化,并对算法结果质量进行定量评价.结果表明,与已有智能MPRM最小化算法相比,所提出算法具有更好的全局收敛能力,能够提高算法结果质量.

MPRM minimization algorithm based on SADPSO

A hybrid simulated annealing and discrete particle swarm optimization (SADPSO) based intelligent algorithm is proposed for mixed-polarity Reed-Muller (MPRM) logic minimization. The proposed algorithm combines simulated annealing (SA) and discrete particle swarm optimization (DPSO) by applying SA to the best solution obtained by DPSO to help the algorithm escape from local minima. Twenty-three MCNC benchmark circuits are minimized respectively by using the proposed algorithm and existing intelligent MPRM minimization algorithms, and the qualities of algorithm results are evaluated quantitatively for these algorithms. The results show that,compared to existing intelligent MPRM minimization algorithms, the proposed algorithm has better ability of global convergence, and can improve the qualities of algorithm results.