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一种新量子进化算法及其在函数优化中的应用 被引量：5

Novel Quantum-inspired Evolutionary Algorithm and Its Application to Numerical Optimization Problems
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摘要 基于量子计算理论和进化理论,提出了一种新的量子进化算法—基于实数编码的量子进化算法(RQEA)。不同于传统进化算法的单点编码和量子进化算法的量子比特编码,算法以实数矩形区域来表示基因,一条染色体携带多个个体信息。利用量子态叠加和相干机理,通过叠加、变异及自学习来完成进化过程。实验表明,该算法在函数优化上具有优异的性能。 A novel kind of algorithm, Real-coded Quantum-inspired Evolutionary Algorithm （RQEA）, was proposed based on evolution theory and quantum computation. Different from representation of single point coding and quantum-bits coding, rectangle region was employed in RQEA to represent gene. A chromosome took several individuals information. Quantum computation mechanism was adopted. Interference operator, mutation operator and self-learning operator were proposed to accelerate evolution process. The result of experiments shows that RQEA has a strong ability of global optimization and high convergence speed.
作者 覃朝勇 郑建国
机构地区 东华大学工商管理学院 广西大学数学与信息科学学院
出处 《系统仿真学报》 CAS CSCD 北大核心 2009年第10期2862-2865,2871,共5页 Journal of System Simulation
基金 国家自然科学基金(60372072) 上海自然科学基金(06ZR14004)
关键词 量子计算 进化算法 量子染色体 实数编码 quantum computation evolutionary algorithm quantum-bit chromosome real-coded
分类号 TP391.9 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

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二级参考文献16

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共引文献182

同被引文献35

  • 1王凡,张耀良.关于“权”及确定权重分配方法的探讨[J].系统工程,1993,11(5):11-14. 被引量：24
  • 2李士勇,李盼池.基于实数编码和目标函数梯度的量子遗传算法[J].哈尔滨工业大学学报,2006,38(8):1216-1218. 被引量：61
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  • 4HAN K H,KIM J H.Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[J].IEEE Transactions on Evolutionary Computation,2002,6(6):580-593.
  • 5HAN K H,KIM J H.Quantum-inspired evolutionary algorithm witha new termination criterion,Hegate and two-phase scheme[J].IEEE Transactions on Evolutionary Computation,2004,8(2):156 -169.
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  • 9Yang S,Wang M,Jiao L.A novel quantum evolutionary algorithm and its application[C].Proc of IEEE Congress on Evolutionary Computation.Vancouver,Canada:IEEE Press,2004:820-826.
  • 10Cruz A V A,Vellasco M B R,Pacheco M A C.Quantum-inspired evolutionary algorithm for numerical optimization[C].Proc of IEEE Congress on Evolutionary Computation.Vancouver,Canada:IEEE Press,2006:2630-2637.

引证文献5

二级引证文献14

系统仿真学报
2009年 第10期

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