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计算机工程 ›› 2012, Vol. 38 ›› Issue (11): 180-182,185. doi: 10.3969/j.issn.1000-3428.2012.11.055

• 人工智能及识别技术 • 上一篇    下一篇

基于CS算法的Markov模型及收敛性分析

王 凡,贺兴时,王 燕,杨松铭   

  1. (西安工程大学理学院,西安 710048)
  • 收稿日期:2011-09-28 出版日期:2012-06-05 发布日期:2012-06-05
  • 作者简介:王 凡(1984-),女,硕士研究生,主研方向:智能优化算法;贺兴时,教授;王 燕、杨松铭,硕士研究生
  • 基金资助:
    陕西省教育厅自然科学基金资助项目(2010JK563);西安工程大学研究生创新基金资助项目(chx110922)

Markov Model and Convergence Analysis Based on Cuckoo Search Algorithm

WANG Fan, HE Xing-shi, WANG Yan, YANG Song-ming   

  1. (School of Science, Xi’an Polytechnic University, Xi’an 710048, China)
  • Received:2011-09-28 Online:2012-06-05 Published:2012-06-05

摘要: 为完善布谷鸟搜索(CS)算法的收敛性理论,建立CS算法的Markov链模型,分析该Markov链的有限齐次性,在此基础上通过分析鸟窝位置的群体状态转移过程,指出随机序列将进入最优状态集,同时证明CS算法满足随机搜索算法全局收敛的2个条件。通过仿真实验验证CS算法可收敛于全局最优,从而确保CS算法的全局收敛性。

关键词: 启发式算法, 布谷鸟搜索, Markov链, 状态转移, 全局收敛性

Abstract: In order to perfect the convergence theory of Cuckoo Search(CS) algorithm, the Markov chain model of the CS algorithm is established and the property of the limited and homogeneous of Markov chain is analyzed. On the basis of this, through the analysis of the state transition process of a group of nest position, the stochastic sequence enters to the optimal state set. And CS algorithm meets the global convergence qualification of random search algorithms. Simulation experimental results show that CS algorithm achieves the global optimization, and the global convergence is ensured.

Key words: heuristic algorithm, Cuckoo Search(CS), Markov chain, state transition, global convergence

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