Soft Computing

, Volume 22, Issue 2, pp 437–451 | Cite as

Artificial bee colony algorithm with an adaptive greedy position update strategy

Methodologies and Application

Abstract

Artificial bee colony (ABC) is a recent swarm intelligence algorithm. There have been some greedy ABC variants developed to enhance the exploitation capability, but greedy variants are usually less reliable and may cause premature convergence, especially without proper control on the greediness degree. In this paper, we propose an adaptive ABC algorithm (AABC), which is characterized by a novel greedy position update strategy and an adaptive control scheme for adjusting the greediness degree. The greedy position update strategy incorporates the information of top t solutions into the search process of the onlooker bees. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the greediness degree to fit for different optimization scenarios, the proposed adaptive control scheme further adjusts the size of top solutions for selection in each iteration of the algorithm. The adjustment is based on considering the current search tendency of the bees. This way, by combining the greedy position update process and the adaptive control scheme, the convergence performance and the robustness of the algorithm can be improved at the same time. A set of benchmark functions is used to test the proposed AABC algorithm. Experimental results show that the components of AABC can significantly improve the performance of the classic ABC algorithm. Moreover, the AABC performs better than, or at least comparably to, some existing ABC variants as well as other state-of-the-art evolutionary algorithms.

Keywords

Adaptive parameter control Artificial bee colony (ABC) Global optimization Swarm intelligence 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61502544, 61402545, and 61332002).

Compliance with ethical standards

Conflict of interest

Wei-Jie Yu, Zhi-Hui Zhan, and Jun Zhang declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Information ManagementSun Yat-sen UniversityGuangzhouChina
  2. 2.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina

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