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计算机工程 ›› 2011, Vol. 37 ›› Issue (20): 223-226. doi: 10.3969/j.issn.1000-3428.2011.20.077

• 人工智能及识别技术 • 上一篇    下一篇

量子进化算法原理及改进策略研究

魏 娜 1,黄学宇 2,刘守东 1   

  1. (1. 空军工程大学训练部教育技术中心,西安 710051; 2. 空军工程大学导弹学院,西安 713800)
  • 收稿日期:2011-04-29 出版日期:2011-10-20 发布日期:2011-10-20
  • 作者简介:魏 娜(1981-),女,讲师、硕士,主研方向:智能计算,遗传算法;黄学宇,讲师、博士研究生;刘守东,副教授

Research on Principle and Improved Strategy of Quantum Evolutionary Algorithm

WEI Na 1, HUANG Xue-yu 2, LIU Shou-dong 1   

  1. (1. Educate Technology Center of Exercitation Department, Air Force Engineering University, Xi’an 710051, China; 2. Missile Institute, Air Force Engineering University, Xi’an 713800, China)
  • Received:2011-04-29 Online:2011-10-20 Published:2011-10-20

摘要: 针对传统进化算法存在收敛速度慢和未成熟收敛的问题,将进化算法与量子理论相结合,提出一种量子进化算法。使用量子比特编码染色体,构造一种新的用于普通染色体的全干扰交叉操作。实验证明,该算法能带来丰富的种群,使其以大概率向优良模式进化,从而加快算法的收敛速度,同时还能避免种群陷于一个局部最优,有效防止早熟。

关键词: 量子优化, 量子进化, 量子遗传, 遗传算法, 进化策略, 进化规划

Abstract: Aiming at the defects of the low convergence rate and the immature convergence in the traditional evolutionary algorithm, this paper combines quantum optimization algorithms with evolutionary algorithm, puts forward the quantum evolutionary algorithm. It adopts quantum bits code chromosome, and constructs a new entirety interference crossover which acts on general chromosome crossover manipulation. Experiment proves that the algorithm can bring abundant population, improve the choiceness mode probability of population evolution, and quicken the convergence rate. It can avoid population fall into local optimums, and the precocity.

Key words: quantum optimization, quantum evolutionary, quantum genetic, genetic algorithm, evolution strategy, evolutionary programming

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