Delay and Area Optimization for MPRM Circuits Based on Multi-strategy Discrete Particle Swarm Optimization

FU Qiang; WANG Peng-jun; TONG Nan; WANG Ming-bo; ZHANG Hui-hong

doi:10.3969/j.issn.0372-2112.2016.05.027

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Delay and Area Optimization for MPRM Circuits Based on Multi-strategy Discrete Particle Swarm Optimization

更新时间：2025-07-16

- Delay and Area Optimization for MPRM Circuits Based on Multi-strategy Discrete Particle Swarm Optimization
- Acta Electronica Sinica Vol. 44, Issue 5, Pages: 1202-1207(2016)
- 作者机构：
  
  1. 宁波大学电路与系统研究所,浙江,宁波,315211
  2. 宁波大学科学技术学院,浙江,宁波,315212
  3. 宁波大学电路与系统研究所,浙江,宁波,315211
  4. 宁波大学科学技术学院,浙江,宁波,315212
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61306041, No.61234002);Ningbo Natural Science Fund (No.2014A610069);Research Program of Education Department of Zhejiang Province (No.Y201326770)
- DOI：10.3969/j.issn.0372-2112.2016.05.027
  CLC： TP391.72
- Published Online：25 May 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
FU Qiang, WANG Peng-jun, TONG Nan, et al. Delay and Area Optimization for MPRM Circuits Based on Multi-strategy Discrete Particle Swarm Optimization[J]. Acta Electronica Sinica, 2016, 44(5): 1202-1207.
DOI：

FU Qiang, WANG Peng-jun, TONG Nan, et al. Delay and Area Optimization for MPRM Circuits Based on Multi-strategy Discrete Particle Swarm Optimization[J]. Acta Electronica Sinica, 2016, 44(5): 1202-1207. DOI： 10.3969/j.issn.0372-2112.2016.05.027.

摘要

针对大规模混合极性Reed-Muller(Mixed Polarity Reed-Muller

MPRM)逻辑电路的延时与面积优化

提出一种基于多策略离散粒子群优化(Multi-Strategy Discrete Particle Swarm Optimization

MSDPSO)的极性搜索方法.在MSDPSO算法中

对粒子进行团队划分

每个团队既执行不同策略

又相互联系

并行完成探索与开发的双重任务.同时在进化过程中采用高斯调整来激活寻优能力较差的粒子.结合MSDPSO算法和列表极性转换技术

对大规模MPRM电路进行延时与面积极性搜索.最后对PLA格式的MCNC Benchmark电路进行算法性能测试

结果验证了MSDPSO算法的有效性.与离散粒子群优化(Discrete Particle Swarm Optimization

DPSO)算法的优化结果相比较

MSDPSO算法获取的电路延时平均缩短8.43%

面积平均节省38.36%.

Abstract

In order to improve the delay and area design of large-scale MPRM circuits

the multi-strategy discrete particle swarm optimization(MSDPSO)is proposed.In MSDPSO

the particles were divided into several teams with different strategy

and each team cooperated with others to promote the exploration and exploitation of the particle population.Meanwhile

the Gaussian adjustment was adopted to activate the worse individuals.Combined with MSDPSO and tabular technique

the best polarity of delay and area was searched for large-scale MPRM circuits.MCNC Benchmarks with PLA format are tested to verify the effectiveness of the MSDPSO

and the results show that MSDPSO has achieved an average saving of 8.46% and 38.73% on delay and area respectively in comparison with the DPSO.

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