Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization

Tansel Dokeroglu*, Selen Pehlivan, Bilgin Avenoglu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO) for the multi-dimensional numerical problems. The best practices of ABC and TLBO are implemented to provide robust algorithms on a distributed memory computation environment using MPI libraries. Island parallel versions of the proposed hybrid algorithm are observed to obtain much better results than those of sequential versions. Parallel pseudorandom number generators are used to provide diverse solution candidates to prevent stagnation into local optima. The performances of the proposed hybrid algorithms are compared with eight different metaheuristics algorithms of particle swarm optimization, differential evolution variants, ABC variants and evolutionary algorithm. The empirical results show that the new hybrid parallel algorithms are scalable and the best performing algorithms when compared to the state-of-the-art metaheuristics.

Original languageEnglish
Pages (from-to)7026-7046
Number of pages21
JournalJOURNAL OF SUPERCOMPUTING
Volume76
Issue number9
Early online date9 Jan 2020
DOIs
Publication statusPublished - Sep 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Artificial bee colony
  • Hybrid
  • Parallel
  • TLBO

Fingerprint

Dive into the research topics of 'Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization'. Together they form a unique fingerprint.

Cite this