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

Dokeroglu T., Pehlivan Tort S., Avenoğlu B.

Journal of Supercomputing, vol.76, no.9, pp.7026-7046, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 76 Issue: 9
  • Publication Date: 2020
  • Doi Number: 10.1007/s11227-019-03127-7
  • Journal Name: Journal of Supercomputing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.7026-7046
  • Keywords: Hybrid, Artificial bee colony, TLBO, Parallel, LEARNING-BASED OPTIMIZATION
  • TED University Affiliated: Yes


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.