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Soft Computing

, Volume 23, Issue 24, pp 13161–13182 | Cite as

Improved quick artificial bee colony (iqABC) algorithm for global optimization

  • Selcuk Aslan
  • Hasan BademEmail author
  • Dervis Karaboga
Methodologies and Application

Abstract

Artificial bee colony (ABC) algorithm inspired by the complex behaviors of honey bees in foraging is one of the most significant swarm intelligence-based meta-heuristics and has been successfully applied to a number of numerical and combinatorial optimization problems. In this study, for increasing the early convergence performance of the ABC algorithm while protecting the qualities of the final solutions, a new exploitation mechanism from the best food source that is managed by the number of evaluations is described and its efficiency on both employed and onlooker bee phases is analyzed. The results of the experimental studies obtained from a set of benchmark problems showed that the ABC algorithm with the proposed method performs significantly better than the standard implementation of ABC algorithm and its other variants in terms of convergence speed and solution quality especially for the difficult problems that should be solved before completion of the relatively small number of fitness evaluations.

Keywords

Swarm intelligence Artificial bee colony Convergence speed 

Notes

Compliance with ethical standards

Conflict of interest

The 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 GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Ondokuz Mayis UniversitySamsunTurkey
  2. 2.Sutcu Imam UniversityKahramanmarasTurkey
  3. 3.Erciyes UniversityKayseriTurkey

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