量子遗传算法在多输出Reed-Muller逻辑电路最佳极性搜索中的应用

Application of Quantum Genetic Algorithm in Searching for Best Polarity of Multi-Output Reed-Muller Logic Circuits

量子遗传算法是一种融合量子计算和遗传算法优点的智能算法,常用于求解组合优化问题.本文给出多输出RM(Reed-Muller)逻辑电路最佳极性搜索方案,将量子遗传算法应用到多输出固定极性RM电路逻辑优化中.针对量子遗传算法易陷入局部极值的缺陷,结合群体灾变思想,提出一种基于量子遗传算法的多输出RM逻辑电路最佳极性搜索算法.最后对多个大规模PLA格式基准电路测试表明:该算法与基于遗传算法的最佳极性搜索相比,在优化能力、寻优性能和收敛速度等方面都有不同程度的提高.

QGA（Quantum Genetic Algorithm） is an intelligent algorithm which colligates the advantages of quantum computation and GA（ Genetic Algorithm）, which is often used to solve combinatorial problems.In this paper,the method of best polarities of multi-output RM （Reed-Muller） logic circuits is given, and QGA is applied to the optimization of multi-output fixed-polarity RM logic, circuits. To deal with the defects of the easily immerging in partial minimum frequently, this paper proposes a QGA based multi-output RM best polarity search algorithm which combined with community disaster. Finally, through several large-scale PLA format benchmarks testing,results show that QGA based search algorithm has higher performance than GA based in optimization, search and convergence.