Soft Computing

, Volume 22, Issue 7, pp 2217–2243 | Cite as

Modified Gbest-guided artificial bee colony algorithm with new probability model

  • Laizhong Cui
  • Kai Zhang
  • Genghui Li
  • Xianghua Fu
  • Zhenkun Wen
  • Nan Lu
  • Jian Lu
Methodologies and Application

Abstract

Artificial bee colony (ABC) is a very effective and efficient swarm-based intelligence optimization algorithm, which simulates the collective foraging behavior of the honey bees. However, ABC has strong exploration ability but poor exploitation ability because its solution search equation performs well in exploration but badly in exploitation. In order to enhance the exploitation ability and obtain a better balance between exploitation and exploration, in this paper, a novel search strategy which exploits the valuable information of the current best solution and a novel probability model which makes full use of the other good solutions on onlooker bee phase are proposed. To be specific, in the novel search strategy, a parameter P is used to control which search equation to be used, the original search equation of ABC or the new proposed search equation. The new proposed search equation utilizes the useful information from the current best solution. In the novel probability model, the selected probability of the good solution is absolutely significantly larger than that of the bad solution, which makes sure the good solutions can attract more onlooker bees to search. We put forward a new ABC variant, named MPGABC by combining the novel search strategy and probability model with the basic framework of ABC. Through the comparison of MPGABC and some other state-of-the-art ABC variants on 22 benchmark functions, 22 CEC2011 real-world optimization problems and 28 CEC2013 real-parameter optimization problems, the experimental results show that MPGABC is better than or at least comparable to the competitors on most of benchmark functions and real-world problems.

Keywords

Artificial bee colony algorithm Search strategy Probability model Global optimization 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants 61402294, 61472258 and 61572328, Guangdong Natural Science Foundation under Grant S2013040012895, Foundation for Distinguished Young Talents in Higher Education of Guangdong, China, under Grant 2013LYM_0076, Major Fundamental Research Project in the Science and Technology Plan of Shenzhen under Grants KQCX20140519103756206, JCYJ20140418091413526, JCYJ20140509172609162, JCYJ20140828163633977, JCYJ20140418181958501, JCYJ20150630105452814, JCYJ20160310095523765 and JCYJ20160307111232895. The Open Research Fund of China-UK Visual Information Processing Lab.

Compliance with ethical standards

Conflict of interest

All authors 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 2017

Authors and Affiliations

  • Laizhong Cui
    • 1
  • Kai Zhang
    • 1
  • Genghui Li
    • 1
  • Xianghua Fu
    • 1
  • Zhenkun Wen
    • 1
  • Nan Lu
    • 1
  • Jian Lu
    • 2
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China
  2. 2.College of Mathematics and StatisticsShenzhen UniversityShenzhenPeople’s Republic of China

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