计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2550-2552.DOI: 10.11772/j.issn.1001-9081.2013.09.2550

• 人工智能 • 上一篇    下一篇

基于云模型的实数编码量子进化算法

李国柱   

  1. 西安文理学院 物理与机械电子工程学院,西安 710065
  • 收稿日期:2013-03-18 修回日期:2013-04-26 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 李国柱
  • 作者简介:李国柱(1976-),男,山西长治人,讲师,硕士,主要研究方向:智能算法。
  • 基金资助:

    陕西省教育厅科学研究计划项目

Real-coded quantum evolutionary algorithm based on cloud model

LI Guozhu   

  1. School of Physics and Mechatronics Engineering, Xi'an University of Arts and Science, Xi'an Shaanxi 710065, China
  • Received:2013-03-18 Revised:2013-04-26 Online:2013-10-18 Published:2013-09-01
  • Contact: LI Guozhu

摘要: 针对量子进化算法易陷入局部最优和求解精度不高的缺点,利用云模型具有随机性和稳定倾向性的特点,提出了一种基于云模型的实数编码量子进化算法。该算法利用单维云变异进行全局快速搜索,利用多维云进化增强算法局部搜索能力,探索全局最优解。依据算法的进化过程动态调整搜索范围并复位染色体,可以加提高敛速度,并防止陷入局部最优。仿真结果表明,该算法搜索精度和效率得到提高,适合求解复杂函数优化问题。

关键词: 云模型, 量子进化算法, 实数编码, 全局优化, 函数优化

Abstract: To deal with the problems of easily falling into local optimum and low accuracy in the quantum evolutionary algorithm, a real-coded quantum evolutionary algorithm based on cloud model (CRCQEA) was proposed by using the characteristics of cloud model randomness and stable disposition. The algorithm used a single-dimensional variation of cloud for rapid global search, and used a multi-dimensional cloud evolution for enhancing local search ability to explore the global optimal solution. Dynamic adjustment of search range and resetting of the chromosomes, on the basis of the evolutionary process of algorithm, can speed up the convergence and prevent falling into local optimum. The simulation results show that the algorithm improves search accuracy and efficiency, and the algorithm is well suitable for the complex function optimization.

Key words: cloud model, quantum evolutionary algorithm, real-coded, global optimization, function optimization

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