基于MODCPSO算法的三值FPRM电路面积与延时优化
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摘要
针对三值固定RM(fixed polarity Reed-Muller,FPRM)逻辑电路面积与延时综合优化问题进行了研究,提出了一种基于竞争行为多目标离散粒子群算法(multi-objective discrete competitive particle swarm optimization,MODCPSO)的极性搜索方案。首先在MODCPSO算法中引入竞争行为机制,将种群划分为不同的团队,从各个团队中随机抽取两个粒子进行比较,令较差的粒子向着较好的粒子进行速度和位置的更新;同时引入变异机制,令种群粒子能够跳出局部最优解,继续更新进化;然后结合三值FPRM极性转换技术和MODCPSO算法搜索电路面积与延时的最佳极性;最后利用PLA格式的MCNC Benchmark电路实现算法测试,并与DPSO、MODPSO算法进行了性能对比。实验结果验证了MODCPSO算法的有效性。
To solve the area and delay synthesis optimization problem of ternary FPRM circuits,this paper proposed a multiobjective discrete competitive particle swarm optimization(MODCPSO). Firstly,the algorithm introduced the competition mechanism. It divided the population into different teams,and randomly selected two particles from each team for comparison,so that the poor particles could update their speed and position towards the better particles. Meanwhile,the particles could avoid falling into local minima through mutation mechanism. Secondly,it combined with the polarity conversion technique,MODCPSO would search the best polarity from the ternary FPRM circuits. Finally,the experiment result of PLA format MCNC Benchmark circuits shows that the MODCPSO algorithm has good performance in optimization,compared with the other algorithms.
To solve the area and delay synthesis optimization problem of ternary FPRM circuits,this paper proposed a multiobjective discrete competitive particle swarm optimization(MODCPSO). Firstly,the algorithm introduced the competition mechanism. It divided the population into different teams,and randomly selected two particles from each team for comparison,so that the poor particles could update their speed and position towards the better particles. Meanwhile,the particles could avoid falling into local minima through mutation mechanism. Secondly,it combined with the polarity conversion technique,MODCPSO would search the best polarity from the ternary FPRM circuits. Finally,the experiment result of PLA format MCNC Benchmark circuits shows that the MODCPSO algorithm has good performance in optimization,compared with the other algorithms.
引文
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[12]王振海,汪鹏君,俞海珍,等.基于PSO算法的FPRM电路延时与面积优化[J].电路与系统学报,2012,17(5):75-80.(Wang Zhenhai,Wang Pengjun,Yu Haizhen,et al. Delay and area optimization for FPRM circuits based on PSO algorithm[J]. Journal of Circuits and Systems,2012,17(5):75-80.)
[2] Wang Pengjun,Li Kangping,Zhang Hhuihong. PMGA and its application in area and power optimization for ternary FPRM circuit[J].Journal of Semiconductors,2016,37(1):126-130.
[3]马雪娇,厉琼莹,夏银水,等.基于双逻辑门级图形表示的功耗优化技术[J].计算机辅助设计与图形学学报,2017,29(3):509-518.(Ma Xuejiao,Li Qiongying,Xia Yinshui,et al. Power optimization technique based on dual-logic diagram expression at gate level[J]. Journal of Computer-Aided Design and Computer Graphics,2017,29(3):509-518.)
[4]卜登立,江建慧.基于Pareto支配的MPRM电路面积与可靠性优化[J].电子学报,2016,44(11):2653-2659.(Bu Dengli,Jiang Jianhui. Pareto dominance based area and reliability optimization of MPRM circuit[J]. Acta Electronic Sinica,2016,44(11):2653-2659.)
[5]符强,汪鹏君,童楠,等.基于MODPSO算法的FPRM电路多约束极性优化方法[J].电子与信息学报,2017,39(3):717-723.(Fu Qiang,Wang Pengjun,Tong Nan,et al. Multi-constrained polarity optimization of large-scale FPRM circuits based on multi-objective discrete particle swarm optimization[J]. Journal of Electronic and Information Technology,2017,39(3):717-723.)
[6] Wang Mingan,Shuo Feng,He Chunhui,et al. An artificial immune system algorithm with social learning and its application in industrial PID controller design[J]. Mathematical Problems in Engineering,2017,2017:Article ID 3959474.
[7] Ni Qingjian,Pan Qianqian,Du Huimin,et al. A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization[J]. IEEE/ACM Trans on Computational Biology and Bioinformatics,2017,14(1):76-84.
[8] Keshanchi B,Souri A,Navimipour N J. An improved genetic algorithm for task scheduling in the cloud environments using the priority queues:formal verification simulation,and statistical testing[J].Journal of Systems and Software,2017,124(2):1-21.
[9] Xu Suhui,Mu Xiaodong,Dong Chai,et al. Multi-objective quantumbehaved particle swarm optimization algorithm with double-potential well and share-learning[J]. Optik:International Journal for Light and Electron Optics,2016,127(12):4921-4927.
[10] Dai Cai,Wang Yuping,Ye Miao. A new multi-objective particle swarm optimization algorithm based on decomposition[J]. Information Sciences,2015,325(12):541-557.
[11]Yu Haizhen,Wang Pengjun,Zhang Yuejun. A ternary polarity conversion technique for fixed polarity Reed-Muller expansions[C]//Proc of International Conference on Signal Processing. Piscataway,NJ:IEEE Press,2016:1-5.
[12]王振海,汪鹏君,俞海珍,等.基于PSO算法的FPRM电路延时与面积优化[J].电路与系统学报,2012,17(5):75-80.(Wang Zhenhai,Wang Pengjun,Yu Haizhen,et al. Delay and area optimization for FPRM circuits based on PSO algorithm[J]. Journal of Circuits and Systems,2012,17(5):75-80.)