作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

蝙蝠算法的Markov链模型分析

尚俊娜 a,程涛 a,岳克强 b,盛林 a   

  1. (杭州电子科技大学 a.通信工程学院; b.电子信息学院,杭州 310018)
  • 收稿日期:2016-06-01 出版日期:2017-07-15 发布日期:2017-07-15
  • 作者简介:尚俊娜(1979—),女, 副教授、博士,主研方向为人工智能、信号处理;程涛(通信作者),硕士研究生;岳克强,讲师、博士;盛林,硕士研究生。
  • 基金资助:
    浙江省自然科学基金青年基金(LQ13F010010);浙江省重点科技创新团队项目(2013TD03);浙江省“电子科学与技术”重中之重学科开放基金(GK13020320003/004)。

Markov Chain Model Analysis of Bat Algorithm

SHANG Junna  a,CHENG Tao  a,YUE Keqiang  b,SHENG Lin  a   

  1. (a.College of Telecommunication Engineering; b.College of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China)
  • Received:2016-06-01 Online:2017-07-15 Published:2017-07-15

摘要: 针对当前蝙蝠算法的性能改进缺少严谨的收敛性证明,导致算法的改进不具备明确的理论意义的问题,从数学概率以及蝙蝠算法状态转移满足Markov过程的角度为出发点,通过建立合理的Markov链模型研究蝙蝠个体状态的转移行为,论证蝙蝠群体状态空间具有可约性和齐次性,从理论上证明蝙蝠算法满足随机算法的收敛准则,保证算法能100%收敛到全局最优解。

关键词: Markov链, 蝙蝠算法, 全局收敛性, 转移概率, 全局最优解

Abstract: The performance improvement of the current Bat Algorithm(BA) lacks rigorous convergence theory, so that the blindness caused by the improvement of the algorithm does not have general theoretical significance.To solve this problem, this paper starts from the perspective of mathematical probability and the state transition of the BA meeting the Markov process.Through the establishment of a reasonable Markov chain model, the transfer behavior of individual status of bats is studied.It proves that the bat population state space is reducible and homogeneous, and the BA satisfies the convergence criterion of stochastic algorithm theoretically.It can converge to the global optimal solution with the probability of 100%.

Key words: Markov chain, Bat Algorithm(BA), global convergence, transition probability, global optimal solution

中图分类号: