Parallel Approaches for the Artificial Bee Colony Algorithm

Parpinelli, Rafael Stubs; Benítez, César Manuel Vargas; Lopes, Heitor Silvério

doi:10.1007/978-3-642-17390-5_14

Cited by 29 publications

(19 citation statements)

References 22 publications

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“…These hybrid strategies are expected to provide more efficient and flexible approaches to solve complex problems that would be very difficult to solve with simple methods. Some related works have shown good results when using hybrid bio-inspired search strategies cooperatively [3,18,27,30,36,47].…”

Section: Introductionmentioning

confidence: 95%

A computational ecosystem for optimization: review and perspectives for future research

Parpinelli

Lopes

2014

Memetic Comp.

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Nature exhibits extremely diverse, dynamic, robust, complex and fascinating phenomena and, since long ago, it has been a great source of inspiration for solving hard and complex problems in computer science. Hence, the search for plausible biologically inspired ideas, models and computational paradigms always drew the interest of computer scientists. It is worth mentioning that most bio-inspired algorithms only focuses on and took inspiration from specific aspects of the natural phenomena. However, in nature, biological systems are interlinked to each other, e.g., biological ecosystems. The ecosystem as a whole can be composed by species that respond to environmental and ecological stimuli. This work reviews the theoretical foundations and applications of a computational ecosystem for optimization, named ECO. Also, as some concepts and processes inherent to biological ecosystems have already been explored in the ECO approach, some related works are described. Finally, several future research directions are pointed.

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Section: Introductionmentioning

confidence: 95%

A computational ecosystem for optimization: review and perspectives for future research

Parpinelli

Lopes

2014

Memetic Comp.

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“…More specifically, researchers in [24] proposed three parallel models for the ABC algorithm, master-worker, multi-hive with migrations and hybrid hierarchical and they also conducted an peformance evaluation on a cluster of workstations using MPI programming paradigm. In [25], they parallelised ABC algorithm on a multi-core platform with a multiple swarm model where whole colony is divided into four sub-colonies.…”

Section: Introductionmentioning

confidence: 99%

An efficient multi-core implementation of the Jaya optimisation algorithm

Michailidis

2017

International Journal of Parallel, Emergent and Distributed Sys

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In this work, we propose a hybrid parallel Jaya optimisation algorithm for a multi-core environment with the aim of solving large-scale global optimisation problems. The proposed algorithm is called HHCPJaya, and combines the hyper-population approach with the hierarchical cooperation search mechanism. The HHCPJaya algorithm divides the population into many small subpopulations, each of which focuses on a distinct block of the original population dimensions. In the hyper-population approach, we increase the small subpopulations by assigning more than one subpopulation to each core, and each subpopulation evolves independently to enhance the explorative and exploitative nature of the population. We combine this hyper-population approach with the two-level hierarchical cooperative search scheme to find global solutions from all subpopulations. Furthermore, we incorporate an additional updating phase on the respective subpopulations based on global solutions, with the aim of further improving the convergence rate and the quality of solutions. Several experiments applying the proposed parallel algorithm in different settings prove that it demonstrates sufficient promise in terms of the quality of solutions and the convergence rate. Furthermore, a relatively small computational effort is required to solve complex and large-scale optimisation problems.

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“…Subotic ve digerleri, bütün bir koloniyi alt kolonilere ayırmak yerine, çoklu kolonilerin paralel çalıştırıldıgı ve belirli göç periyotlarında en iyi çözümlerin koloniler arasında degiştirildigi bir model önermiştir [6]. Parpinelli ve digerleri, çok kolonili 978-1-5090-1679-2/16/$31.00 c 2016 IEEE paralel senaryo için farklı göç yaklaşımları ile ABC algoritması için yeni paralel modeller önermiştir [7]. ABC algoritması için ayrıntılı paralelleştirme senaryoları Baştürk ve Akay tarafından gerçekleştirilmiştir.…”

Section: Introductionunclassified

A new emigrant creation strategy based on local best sources for parallel Artificial Bee Colony algorithm

Karaboğa

Aslan

2016

2016 24th Signal Processing and Communication Application Conference (SIU)

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Özetçe -Arıların yiyecek arama davranışlarını modelleyen Yapay Arı Koloni algoritması, en önemli sürü zekası temelli optimizasyon algoritmaları arasında yer alır. Diger popülasyon temelli evrimsel hesaplama yaklaşımlarında oldugu gibi, Yapay Arı Koloni algoritması da dagıtık sistemler üzerinde paralel modellenmeye uygundur. Bu çalışmada, alt kolonilere ayrılarak farklı hesaplama dügümleri üzerinde eş zamanlı çalıştırılan Yapay Arı Koloni algoritması için, alt koloniler arasında dagıtılacak çözümün belirlenmesi adına yeni bir yaklaşım sunulmuştur. Farklı sayıda hesaplama dügümü üzerinde paralelleştirilen Yapay Arı Koloni algoritmasının önerilen yaklaşımı, algoritmanın seri eşdegeri ile fonksiyon sonuçları ve yürütme zamanları açısından karşılaştırılmış ve önerilen göçmen oluşturma stratejisinin Yapay Arı Koloni algoritmasının yakınsama performansını önemli ölçüde artırdıgı görülmüştür. Anahtar Kelimeler-Yapay Arı Koloni algoritması, dagıtık sistemler, göçmen seçimi. Abstract-Artificial Bee Colony algorithm, inspired by the foraging behavior of real honey bees, is one of the most important swarm intelligence based optimization algorithms. Like other population based evolutionary computation techniques, ArtificialBee Colony algorithm is suitable for parallelization on distributed architectures. In this paper, we presented a new emigrant creation strategy that is being distributed between subcolonies running simultaneously on the independent compute nodes. The running times and objective function values obtained by the parallelized Artificial Bee Colony algorithm with the proposed model on different number of compute nodes are compared with the sequential counterpart of the algorithm and it is seen that convergence performance of the parallelized Artificial Bee Colony algorithm is significantly improved with the proposed emigrant creation strategy.

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Parallel Approaches for the Artificial Bee Colony Algorithm

Cited by 29 publications

References 22 publications

A computational ecosystem for optimization: review and perspectives for future research

A computational ecosystem for optimization: review and perspectives for future research

An efficient multi-core implementation of the Jaya optimisation algorithm

A new emigrant creation strategy based on local best sources for parallel Artificial Bee Colony algorithm

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