Accelerating Artificial Bee Colony Algorithm with Elite Neighborhood Learning

  • Xinyu ZhouEmail author
  • Yunan Liu
  • Yong Ma
  • Mingwen Wang
  • Jianyi Wan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)


Artificial bee colony (ABC) algorithm has been shown good performance over many optimization problems. For some complex optimization problems, however, ABC often suffers from a slow convergence speed, because it is good at exploration but poor at exploitation. To achieve a better tradeoff between the exploration and exploitation capabilities, we introduce the breadth-first search (BFS) framework and depth-first search (DFS) framework into different phases of ABC respectively. The BFS framework is combined with the employed bee phase to focus on the exploration, while the DFS framework is integrated into the onlooker bee phase to concentrate on exploitation. After that, an elite neighborhood learning (ENL) strategy is proposed to enhance the information exchange between the employed bee phase and the onlooker bee phase, because in ABC the employed bees cannot well communicate with the onlooker bees which may also cause slow convergence speed. Extensive experiments are conducted on 22 well-known test functions, and six well-established ABC variants are included in the comparison. The results showed that our approach can effectively accelerate the convergence speed and significantly perform better on the majority of test functions.


Artificial bee colony Breadth-first search framework Depth-first search framework Elite neighborhood learning 



This work is supported by the National Natural Science Foundation of China (Nos. 61603163, 61462045 and 61562042) and the Science and Technology Foundation of Jiangxi Province (No. 20151BAB217007).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xinyu Zhou
    • 1
    Email author
  • Yunan Liu
    • 1
  • Yong Ma
    • 1
  • Mingwen Wang
    • 1
  • Jianyi Wan
    • 1
  1. 1.School of Computer and Information EngineeringJiangxi Normal UniversityNanchangChina

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