摘要
针对量子原胞自动机遗传模拟退火算法仿真大型电路的效率低的不足,提出一种可以减小问题规模的局部遗传模拟退火算法。理论验证了可定态细胞的定态规则,对可定态细胞进行定义;采用定态规则计算可定态细胞极化状态与遗传模拟退火方法计算不可定态细胞极化状态相结合,从而有效地减小了问题的规模。通过仿真验证了基于定态规则的遗传模拟退火方法相比单纯遗传模拟退火方法更具优越性,不但加快了量子原胞自动机仿真的收敛速度而且提高了仿真的精确度。
To solve the problem of the inefficient simulation of large quantum cellular automata (QCA) circuits using genetic simulated annealing algorithm, local genetic simulated annealing algorithm that can reduce the size of the problem is proposed. Firstly, a proof for the polarized rule is provided which used to judge the polarization of cells, and what is a polarized cell is de- fined, the algorithm combines two ways to significantly reduce the problem size including employing the polarized rule to calculate the polarization of polarized cells and calculating unpolarized cells by means of genetic simulated annealing algorithm. Lastly, ex- perimental results show genetic simulated annealing algorithm based on the polarized rule performs better than independent ge- netic simulated annealing algorithm in terms of computational precision and convergence speed.
出处
《计算机工程与设计》
CSCD
北大核心
2014年第2期609-614,共6页
Computer Engineering and Design
基金
2012年教育部科学技术研究重点基金项目(212094)
江西省2012年度研究生创新专项资金基金项目(YC2012-S075)
2013年度江西省教育厅科技计划基金项目(GJJ13361
GJJ13338)
2012年江西省科技计划基金项目(2012BBE50086)
南昌市科技计划基金项目(2012-KJZC-GY-CXYHZKF-001
2011-DWHZ-HKZZ-001)