摘要
电路划分是VLSI物理设计过程中的一个关键阶段.该问题本质上是一个NP困难的组合优化问题.针对该问题,提出了一种带FM策略的混合粒子群优化算法.引入遗传算法的两点交叉算子和随机两点交换变异算子,保证了粒子在位置更新后依然可行;为了提高算法的局部搜索能力,将具有较强局部搜索能力的FM策略融入算法的位置更新;设计了种群多样性变异策略,提高了种群多样性,避免了易陷入局部最优的缺陷.对ISCAS89标准测试电路的仿真实验结果表明,所构造的算法是有效的.
Circuit partitioning is an important part of any very large scale integration （VLSI） physical design automation, but it is a NP-hard combinatorial optimization problem. In this paper, a hybrid particle swarm optimization algorithm with FM strategy is proposed to approch this problem. Inspired by the mechinism of genetic algorithm （GA）, two-point crossover and random two-point exchange mutation operators have been designed to avoid generating infeasible solutions. To improve the ability of local exploration, FM strategy is applied to the proposed algorithm to update its position. A mutation strategy is also built into the proposed algorithm to achieve better diversity and break away from local optima. Experiments on ISCAS89 benchmark circuits show that the proposed algorithm is efficient.
出处
《软件学报》
EI
CSCD
北大核心
2011年第5期833-842,共10页
Journal of Software
基金
国家自然科学基金(10871221,61070020)
国家重点基础研究发展计划(973)(2006CB805904,2011CB808000)
福建省自然科学基金(A0820002,2009J01284)
福建省科技创新平台计划(2009J1007)
关键词
电路划分
最小割
粒子群优化
超大规模集成电路
circuit partitioning
rain cut
particle swarm optimization
very large scale integration circuit
