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

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dc.contributor.author Dökeroǧlu, Tansel
dc.contributor.author Pehlivan, Selen
dc.contributor.author Avenoğlu, Bilgin
dc.date.accessioned 2020-04-06T15:53:46Z
dc.date.available 2020-04-06T15:53:46Z
dc.date.issued 2020
dc.identifier.issn 0920-8542
dc.identifier.uri https://doi.org/10.1007/s11227-019-03127-7
dc.identifier.uri http://hdl.handle.net/20.500.12485/654
dc.description.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. en_US
dc.language.iso en en_US
dc.subject Artificial bee colony en_US
dc.subject Hybrid en_US
dc.subject Parallel en_US
dc.subject TLBO en_US
dc.title Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization en_US
dc.type Article en_US
dc.relation.journal Journal of Supercomputing en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 21 en_US

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