Hybrid integration of differential evolution with artificial bee colony for global optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we investigate the hybridization of a swarm intelligence algorithm and an evolutionary algorithm, namely, the Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE), to solve continuous optimization problems. This Hybrid Integration of DE and ABC (HIDEABC) technique is based on integrating the DE algorithm with the principle of ABC to improve the neighborhood search for each particle in ABC. The swarm intelligence of the ABC algorithm and the global information obtained by the DE population approach facilitate balanced exploration and exploitation using the HIDEABC algorithm. All algorithms were applied to five benchmark functions and were compared using several different metrics.

Original languageEnglish
Title of host publicationIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Pages15-23
Number of pages9
Publication statusPublished - 2012
Event4th International Joint Conference on Computational Intelligence, IJCCI 2012 - Barcelona
Duration: 2012 Oct 52012 Oct 7

Other

Other4th International Joint Conference on Computational Intelligence, IJCCI 2012
CityBarcelona
Period12/10/512/10/7

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Keywords

  • Artificial Bee Colony
  • Differential Evolution
  • Global Search
  • Hybrid Optimization Methods
  • Local Search
  • Multi-peak Problems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Tam, B. N., Hieu, P. N., & Hasegawa, H. (2012). Hybrid integration of differential evolution with artificial bee colony for global optimization. In IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence (pp. 15-23)