| 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 |
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| 引用本文： | 汪鹏君,李辉,吴文晋,王伶俐,张小颖,戴静.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[J].电子学报,2010,38(5):1058-1063. |
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| 作者姓名： | 汪鹏君 李辉 吴文晋 王伶俐 张小颖 戴静 |
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| 作者单位： | 1.宁波大学电路与系统研究所，浙江宁波 315211；2.复旦大学专用集成电路与系统国家重点实验室，上海 201203;3.浙江大学数字技术及仪器研究所，浙江杭州 310027 |
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| 基金项目： | 国家自然科学基金，中国博士后科学基金，浙江省博士后科研项目，宁波市自然科学基金，浙江省教育厅科学研究项目 |
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| 摘 要： | 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.
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| 关 键 词： | 量子遗传算法 极性搜索 多输出RM电路 逻辑优化 |
| 收稿时间： | 2008-10-23 |
| 修稿时间： | 2009-1-19 |
Application of Quantum Genetic Algorithm in Searching for Best Polarity of Multi-Output Reed-Muller Logic Circuits |
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| WANG Peng-jun,LI Hui,WU Wen-jin,WANG Ling-li,ZHANG Xiao-ying,DAI Jing.Application of Quantum Genetic Algorithm in Searching for Best Polarity of Multi-Output Reed-Muller Logic Circuits[J].Acta Electronica Sinica,2010,38(5):1058-1063. |
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| Authors: | WANG Peng-jun LI Hui WU Wen-jin WANG Ling-li ZHANG Xiao-ying DAI Jing |
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| Affiliation: | 1.Institute of Circuits and Systems,Ningbo University,Ningbo,Zhejiang 315211,China;2.State Key Laboratory of ASIC and System,Fudan University,Shanghai 201203,China;3.Institute of Advanced Digital Technologies and Instrumentation,Zhejiang University,Hangzhou,Zhejiang 310027,China |
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| Abstract: | 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. |
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| Keywords: | quantum genetic algorithm polarity search multi-output RM circuits logic optimize |
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