Affiliations: [a] Key Laboratory of Intelligent Computing & Signal Processing, Anhui University, Ministry of Education, Hefei, Anhui, China | [b] Institute of Wave and Information, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China | [c] School of Information Engineering, West Anhui University, Lu'an 237012, Anhui, China | [d] School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, Jiangsu, China
Correspondence:
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Corresponding author: Mingjuan Xu, School of Information Engineering, West Anhui University, Lu'an 237012, Anhui, China. E-mail:xmj8217@wxc.edu.cn
Abstract: The artificial bee colony (ABC) algorithm is a heuristic optimization
algorithm inspired by the foraging behavior of honey bee swarm. The
different-based update strategy has caused the slow convergence and
precocious phenomena for the ABC algorithm. In order to effectively solve
the problems, a novel update strategy based on the gradient and distribution
information of the population has been proposed in this study. The improved
ABC algorithm makes use of the gradient to guide the population for
discovering the potential optimal solution quickly. With the increasing of
the number of function evaluations, a distribution-based update strategy
also has been used to keep the diversity of the population. The performance
of the proposed ABC algorithm was examined on well-known benchmark
functions. The experimental results show that the proposed algorithm is more
efficient than the basic ABC algorithm and some improved ABC algorithms in
terms of the solution quality, convergence and robustness.
