求解高维函数优化问题的交叉熵蝙蝠算法

doi:10.3969/j.issn.1000-3428.2014.10.032

计算机工程

求解高维函数优化问题的交叉熵蝙蝠算法

李国成^1,2,肖庆宪¹

(1. 上海理工大学管理学院,上海200093; 2. 皖西学院金融与数学学院,安徽六安237012)

收稿日期:2013-09-25 出版日期:2014-10-15 发布日期:2014-10-13
作者简介:李国成(1976 - ),男,副教授、博士研究生,主研方向:智能计算;肖庆宪,教授、博士生导师。
基金资助:
国家自然科学基金资助项目(11171221);上海市一流学科(系统科学)基金资助项目(XTKX2012)。

Cross-entropy Bat Algorithm for Solving High-dimensional Function Optimization Problem

LI Guo-cheng ^1,2,XIAO Qing-xian¹

(1. Business School,University of Shanghai for Science and Technology,Shanghai 200093,China; 2. School of Finance and Mathematics,West Anhui University,Lu’an 237012,China)

Received:2013-09-25 Online:2014-10-15 Published:2014-10-13

摘要/Abstract

摘要： 为改善蝙蝠算法求解高维函数优化问题的全局搜索能力,提高其搜索精度,将交叉熵方法和蝙蝠算法相结合,提出一种交叉熵蝙蝠算法。该算法将基于重要度抽样和Kullback-Leibler 距离的交叉熵全局随机优化算法应用于蝙蝠算法中,采用自适应平滑技术提高算法的收敛速度,利用交叉熵方法的遍历性、自适应和鲁棒性,有效抑制蝙蝠算法的早熟收敛现象。对经典测试函数和CEC2005 测试函数的仿真结果表明,该算法具有全局搜索能力强、求解精度高和鲁棒性等特性。

关键词: 高维函数优化, 蝙蝠算法, 交叉熵, 重要度抽样, 自适应平滑, 协同演化

Abstract: In order to enhance the global search ability of bat algorithm in solving high-dimensional function optimization problems,a Cross-entropy Bat Algorithm(CEBA) is proposed by combining bat algorithm with CE method.The CE global stochastic optimization algorithm which is based on importance sampling and Kullback-Leibler divergence,is embedded into bat algorithm. By using adaptive smoothing technique, CEBA improves the rate of convergence. The improved algorithm fully absorbs the ergodicity,adaptability and robustness of CE,adaptively avoids the stagnancy of population,and enhances the global search ability. Simulated results conducted on classical benchmarks and 10 CEC2005 benchmarks show that the proposed algorithm possesses more powerful global search capacity,higher optimization precision and robustness.

Key words: high-dimensional function optimization, Bat Algorithm(BA), Cross-entropy(CE), importance sampling, adaptive smoothing, co-evolution

中图分类号:

TP183

李国成,肖庆宪. 求解高维函数优化问题的交叉熵蝙蝠算法[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2014.10.032.

LI Guo-cheng,XIAO Qing-xian. Cross-entropy Bat Algorithm for Solving High-dimensional Function Optimization Problem[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2014.10.032.

http://www.ecice06.com/CN/Y2014/V40/I10/168

参考文献

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