Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm

  • Doğan Aydın
  • Tianjun Liao
  • Marco A. Montes de Oca
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7401)


We modify an artificial bee colony algorithm as follows: we make the population size grow over time and apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization benchmark problems. This is not the first time we see that the two aforementioned modifications make an initially non-competitive algorithm obtain state-of-the-art results. In previous work, we have shown that the same modifications substantially improve the performance of particle swarm optimization and ant colony optimization algorithms. Altogether, these results suggest that population growth coupled with local search help obtain high-quality results.


Local Search Local Search Algorithm Benchmark Function Continuous Optimization Local Search Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Doğan Aydın
    • 1
  • Tianjun Liao
    • 2
  • Marco A. Montes de Oca
    • 3
  • Thomas Stützle
    • 2
  1. 1.Dept. of Computer EngineeringDumlupınar UniversityKütahyaTurkey
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  3. 3.Dept. of Mathematical SciencesUniversity of DelawareNewarkUSA

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