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An Efficient Ant Colony Optimization Framework for HPC Environments
dc.contributor.author | González, Patricia | |
dc.contributor.author | Osorio, Roberto | |
dc.contributor.author | Pardo, Xoán C. | |
dc.contributor.author | Banga, Julio R. | |
dc.contributor.author | Doallo, Ramón | |
dc.date.accessioned | 2022-03-22T17:50:54Z | |
dc.date.available | 2022-03-22T17:50:54Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | GONZÁLEZ, Patricia, OSORIO, Roberto R., PARDO, Xoan C., BANGA, Julio R. and DOALLO, Ramón, 2022. An efficient ant colony optimization framework for HPC environments. Applied Soft Computing. 1 January 2022. Vol. 114, p. 108058. DOI 10.1016/j.asoc.2021.108058 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/30140 | |
dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
dc.description.abstract | [Abstract] Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants’ solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale). | es_ES |
dc.description.sponsorship | This work was supported by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039/501100011033), and by Xunta de Galicia and FEDER funds of the EU (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). JRB acknowledges funding from the Ministry of Science and Innovation of Spain MCIN / AEI / 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS), and from MCIN / AEI / 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R (SYNBIOCONTROL). Authors also acknowledge the Galician Supercomputing Center (CESGA) for the access to its facilities. Funding for open access charge: Universidade da Coruña/CISUG | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/30 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104184RB-I00/ES/DESAFIOS ACTUALES EN HPC: ARQUITECTURAS, SOFTWARE Y APLICACIONES/ | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117271RB-C22/ES/REGULACION DINAMICA EN VARIAS ESCALAS DE INGENIERIA METABOLICA: INFERENCIA MULTIMODELO Y OPTIMALIDAD DINAMICA/ | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-82896-C2-2-R/ES/DISEÑO, CARACTERIZACION Y AJUSTE OPTIMO DE BIOCIRCUITOS SINTETICOS PARA BIOPRODUCCION CON CONTROL DE CARGA METABOLICA/ | |
dc.relation.uri | https://doi.org/10.1016/j.asoc.2021.108058 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Combinatorial optimization | es_ES |
dc.subject | Metaheuristics | es_ES |
dc.subject | Ant Colony Optimization | es_ES |
dc.subject | High performance computing | es_ES |
dc.subject | MPI | es_ES |
dc.subject | OpenMP | es_ES |
dc.title | An Efficient Ant Colony Optimization Framework for HPC Environments | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Applied Soft Computing | es_ES |
UDC.volume | 114 | es_ES |
UDC.startPage | 108058 | es_ES |
dc.identifier.doi | 10.1016/j.asoc.2021.108058 |
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