ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.010
An Enhanced Hybridized Artificial Bee Colony Algorithm for Optimization Problems
Abstract— Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm and there
still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial
Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes
enhancing the convergence speed with the information of the global best solution in the onlooker bee phase
and enhancing the information exchange between bees by introducing the mutation operator of Genetic
Algorithm (GA) to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of
ABC, when producing the initial population, the opposition-based learning method is employed. Experiments
are conducted on a set of 6 benchmark functions. The results demonstrate good performance of the proposed
approach in solving complex numerical optimization problems over other four ABC variants.
Index Terms— artificial bee colony algorithm, genetic algorithm, population initialization, search equation
Xingwang Huang, Xuewen Zeng, Rui Han, Xu Wang
National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, CHINA
University of Chinese Academy of Sciences, CHINA
Cite: Xingwang Huang, Xuewen Zeng, Rui Han, Xu Wang, "An Enhanced Hybridized Artificial Bee Colony Algorithm for Optimization Problems," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 60-64, Beijing, 25-27 June, 2017.