萤火虫优化算法理论研究综述  

Overview on Glowworm Swarm Optimization or Firefly Algorithm

作　　者：程美英[1,2] 倪志伟[1,2] 朱旭辉[1,2] 

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]过程优化与智能决策教育部重点实验室,合肥230009

出　　处：《计算机科学》2015年第4期19-24,共6页Computer Science

基　　金：国家自然科学基金项目(71271071;71301041);国家"863"云制造主题项目(2011AA040501);中央高校基本科研业务费专项资金项目(2012HGBZ0208;2011HGBZ1310)资助

摘　　要：萤火虫算法作为一种模仿自然界萤火虫捕食求偶行为的群体智能算法,在离散域优化问题及连续域优化问题中均得到较好的应用,但易"陷入局部最优"这种所有启发式算法均存在的固有缺陷在萤火虫算法中依然存在。从算法的改进、算法的融合、多种群思想以及离散化机理等方面讨论了近些年萤火虫算法的若干改进,最后评述了萤火虫算法未来的研究方向和主要研究内容。The glowworm swarm optimization algorithm（GSO）or firefly algorithm（FA）is one of the intelligence algorithms,which is inspired by the biological behavior of the glowworm attracting mates or preying.It has good performance in the discrete combinational optimization problems and continuous optimization problems.However,it still has some drawbacks such as it＇s easily trapped into local optimal solutions.Starting with the improvement and fusion of the algorithm as well as the discrete mechanism,this paper presented a series of schemes on improving the GSO or FA.Finally,some meaningful remarks on the future research were presented.

关 键 词：萤火虫算法 算法改进 算法融合 离散化机理 趋向同步弱连接 细胞自动机 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]