摘要
针对具有较多输入数的可编程阵列结构纳电子混合极性Reed-Muller电路的面积优化问题,提出一种全离散粒子群优化算法。通过将粒子速度合并到位置更新方程,充分挖掘粒子群优化中的学习因素得到全离散化的粒子更新方程,在此基础之上设计FDPSO算法,并使用探索概率作为算法参数控制算法全局探索与局部开拓间的平衡。对一组输入数大于20的MCNC电路进行优化的实验结果表明,与其他能够用于可编程阵列结构纳电子混合极性Reed-Muller电路面积优化的智能算法相比,全离散粒子群优化算法具有较强的全局收敛能力和结果稳定性,能够以较高时间效率获得较好的优化结果。
In allusion to the area optimization problem of programmable array-structured nanoelectronic mixed-polarity ReedMuller(MPRM) circuit with many input numbers,a fully discretized particle swarm optimization(FDPSO) algorithm is proposed. The learning factors in particle swarm optimization are fully mined by integrating particle velocity into the location update equation to obtain the fully discretized particle update equation. On this basis,FDPSO algorithm is designed and the exploration probability is used as the algorithm parameter to control the balance between global exploration and local exploitation of the algorithm. The optimization experiment for microelectronics center of North Carolina(MCNC)circuit whose input numbers are larger than 20 was carried out. The results show that,in comparison with other intelligent algorithms that can be applied to area optimization of programmable array-structured nanoelectronic MPRM circuit,FDPSO algorithm has stronger global convergence capability and result stability,and can achieve better optimization results with higher time efficiency.
出处
《现代电子技术》
北大核心
2018年第4期78-82,共5页
Modern Electronics Technique
基金
国家自然科学基金(61640412)
流域生态与地理环境监测国家测绘地理信息局重点实验室资助课题(WE2016012)
江西省教育厅科技计划项目(GJJ160746)~~
关键词
纳电子
MPRM电路
面积优化
粒子群优化
更新方程
算法参数
nanoelectronic
MPRM circuit
area optimization
particle swarm optimization
update equation
algorithm parameter
