基于MODPSO算法的FPRM电路多约束极性优化方法

符强; 汪鹏君; 童楠; 王铭波; 张会红

doi:10.11999/JEIT160458

基于MODPSO算法的FPRM电路多约束极性优化方法

doi: 10.11999/JEIT160458

2.
(宁波大学电路与系统研究所宁波 315211) ②(宁波大学科学技术学院宁波 315212)

基金项目:

国家自然科学基金(61306041, 61234002)，十二五浙江省高校重点学科计算机应用技术，浙江省教育厅科研项目(Y201326770)，宁波市自然科学基金(2014A610069，2015A610107)

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出版历程
- 收稿日期: 2016-05-05
- 修回日期: 2016-10-18
- 刊出日期: 2017-03-19

Multi-constrained Polarity Optimization of Large-scale FPRM Circuits Based on Multi-objective Discrete Particle Swarm Optimization

2.
(Institute of Circuits and Systems, Ningbo University, Ningbo 315211, China)

Funds:

The National Natural Science Foundation of China (61306041, 61234002), The Twelfth Five-Year Plan of Zhejiang Province Key Discipline (Computer Application Technology), The Scientific Research Fund of Zhejiang Provincial Education Department (Y201326770), The Ningbo Natural Science Foundation (2014A610069, 2015A610107)

摘要

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优化(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。
- FPRM逻辑电路 /
- 延时与面积优化 /
- 极性搜索 /
- Pareto /
- 多目标离散粒子群算法
Abstract: 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.
- FPRM circuits /
- Delay-area trade-off /
- Polarity search /
- Pareto /
- Multi-Objective Discrete Particle Swarm Optimization (MODPSO)

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