计算机科学与探索2017，Vol.11Issue(3)：491-501,11.DOI:10.3778/j.issn.1673-9418.1604027
具有学习因子的动态搜索烟花算法
Dynamic Search Fireworks Algorithm with Learning Factor
摘要
Abstract
Dynamic search fireworks algorithm (dynFWA) which adopts a dynamic explosion amplitude for core fire-works (CF) has been proved to be a great algorithm for solving optimization problems. However, dynFWA has the dis-advantages that it is easy to fall into local optimal solutions prematurely and has slow convergence rate. In order to im-prove the above mentioned problems, this paper improves conventional dynFWA by embedding two different learning factors which make use of the history successful information, referred as improved dynamic search firework algorithm (IdynFWA). The learning factors take advantages of the information of the best firework in each generation, which makes the fireworks have the ability to learn from the excellent search. Moreover, two different generations of learning factor are beneficial to balance the local search and global search ability. The improved algorithm has been tested on 28 benchmark functions of CEC2013. And the experimental results show that IdynFWA significantly outperforms dyn-FWA, and achieves better performance than both SPSO2011 and DE/rand-to-best/1.关键词
动态搜索烟花算法/爆炸半径/变异算子/学习因子Key words
dynamic search fireworks algorithm/explosion amplitude/mutation operator/learning factor分类
信息技术与安全科学引用本文复制引用
方柳平,汪继文,邱剑锋,朱林波,苏守宝..具有学习因子的动态搜索烟花算法[J].计算机科学与探索,2017,11(3):491-501,11.基金项目
The National Natural Science Foundation of China under Grant No. 61375121 (国家自然科学基金) （国家自然科学基金）
the Provincial Projects of Natural Science for Anhui Universities under Grant No. KJ2013A009 (安徽高校省级自然科学研究项目) （安徽高校省级自然科学研究项目）
the Doctoral Scientific Research Foundation of Anhui University (安徽大学博士启动基金) （安徽大学博士启动基金）
the JIT Scientific Research Program for Introducing Talents under Grant No. jit-rcyj-201505 (金科院引进人才科研项目). （金科院引进人才科研项目）
