A Novel Firefly Algorithm based on Improved Learning Mechanism

被引:0
作者
Fu, Qiang [1 ,2 ]
Liu, Zheng [1 ]
Tong, Nan [1 ]
Wang, Mingbo [1 ]
Zhao, Yiming [1 ]
机构
[1] Ningbo Univ, Coll Sci & Technol, 505 YuXiu Rd, Ningbo 315212, Zhejiang, Peoples R China
[2] Ningbo Univ, Fac Informat Sci & Engn, Ningbo 315211, Zhejiang, Peoples R China
来源
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015) | 2015年 / 117卷
关键词
Firefly Algorithm; Distance Weighting; Chaos; Gaussian Mutation; Learning Mechanism;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
According to the problem of low solution precision, slow convergence speed and some fireflies failure in the traditional firefly algorithm, an improved firefly optimization algorithm is proposed based on the learning mechanism, By chaotic maps, the Firefly's initial position in which dynamic distance weighting is introduced to strength the search ability of algorithm, and adaptive-step scheme is used to balance the dual requirement of local and global optimization. Gaussian mutation is also adopted in the algorithm to help firefly population jump out of local optimum effectively. Experimental results show that the proposed algorithm has rationality and effectiveness.
引用
收藏
页码:1343 / 1351
页数:9
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