中国科学技术大学学报 ›› 2014, Vol. 44 ›› Issue (7): 612-617.DOI: 10.3969/j.issn.0253-2778.2014.07.011

• 论著 • 上一篇    下一篇

一种混沌多样性控制的萤火虫优化算法

徐华丽,苏守宝,严仍荣,马艳   

  1. 1.皖西学院信息工程学院,六安 237012;2.中国科学院南京软件研究院,南京 211169; 3.金陵科技学院计算机学院,南京 211169
  • 收稿日期:2014-03-21 修回日期:2014-04-15 接受日期:2014-04-15 出版日期:2023-05-11 发布日期:2014-04-15
  • 通讯作者: 徐华丽
  • 作者简介:徐华丽(通讯作者),女,1977年生,硕士/副教授. 研究方向:智能计算、数据挖掘. E-mail:hlxu@wxc.edu.cn
  • 基金资助:
    国家自然科学基金(61375121,61075049),安徽省高校自然科学研究重点项目(KJ2014A277),六安市定向委托皖西学院产学研合作项目(2012LWA017),安徽省高校自然科学研究项目(KJ2010B469)资助.

A firefly algorithm with chaotic diversity control

XU Huali, SU Shoubao, YAN Renrong, MA Yan   

  1. 1.School of Information and Engineering, West AnHui University, Luan 237012, China; 2.Nanjing Institute of Software, Chinese Academy of Sciences, Nanajing 211169, China; 3.School of Computers, Jinling Institute of Technology, Nanjing 211169, China
  • Received:2014-03-21 Revised:2014-04-15 Accepted:2014-04-15 Online:2023-05-11 Published:2014-04-15

摘要: 针对基本萤火虫算法存在早熟停滞现象,提出了一种混沌多样性控制的萤火虫优化算法.运用混沌映射产生均匀分布的萤火虫初始位置,获得质量较好的初始解;在搜索过程中对适应值低的部分萤火虫进行混沌扰动,以保持群体活性,减小陷入局部最优的可能性;同时利用真实物理反弹理论对超越边界萤火虫位置进行控制,提高种群的多样性.用标准测试函数测试,实验结果表明,该算法能有效地提高了算法的全局搜索和局部开发能力,寻优精度和收敛速度明显提高.

关键词: 群智能, 萤火虫算法, 混沌, 混沌扰动, 多样性

Abstract: To overcome the disadvantage of premature convergence in the firefly algorithm, a firefly algorithm based on chaos diversity control (CDFA) was proposed. Applying chaotic mapping, CDFA achieved an initial firefly population that is high quality and uniformly distributed; it then disturbed some individuals with low fitness values by chaotic mapping in the process of the search so as to keep the groups activity and reduce the possibility of falling into local optimum; meanwhile, in order to increase the diversity of the population, the proposed algorithm used the physical reflection theory to control the position of the firefly outside the borders. Experimental results of bench mark functions show that CDFA can effectively improve the ability of the global search and local exploitation and has a better optimization precision and convergence rate than the basic FA.

Key words: swarm intelligence, firefly algorithm, chaos, chaos disturbance, diversity

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