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dc.contributor.authorLérida Monsó, Josep Lluís
dc.contributor.authorAgraz Sánchez, Albert
dc.contributor.authorSolsona Tehàs, Francesc
dc.contributor.authorColomer, M. Àngels (Maria Àngels)
dc.date.accessioned2016-05-24T08:57:17Z
dc.date.issued2014
dc.identifier.issn1386-7857
dc.identifier.urihttp://hdl.handle.net/10459.1/57089
dc.description.abstractThe methods used for ecosystem modelling are generally based on differential equations. Nowadays, new computational models based on concurrent processing of multiple agents (multi-agents) or the simulation of biological processes with the Population Dynamic P-System models (PDPs) are gaining importance. These models have significant advantages over traditional models, such as high computational efficiency, modularity and its ability to model the interaction between different biological processes which operate concurrently. By this, they are becoming useful for simulating complex dynamic ecosystems, untreatable with classical techniques. On the other hand, the main counterpart of P-System models is the need for calibration. The model parameters represent the field measurements taken by experts. However, the exact values of some of these parameters are unknown and experts define a numerical interval of possible values. Therefore, it is necessary to perform a calibration process to fit the best value of each interval. When the number of unknown parameters increases, the calibration process becomes computationally complex and storage requirements increase significantly. In this paper, we present a parallel tool (PSysCal) for calibrating next generation PDP models. The results shown that the calibration time is reduced exponentially with the amount of computational resources. However, the complexity of the calibration process and a limitation in the number of available computational resources make the calibration process intractable for large models. To solve this, we propose a heuristic technique (PSysCal + H). The results show that this technique significantly reduces the computational cost, it being practical for solving largemodel instances even with limited computational resources.ca_ES
dc.description.sponsorshipThis work was supported by the Ministry of Education and Science of Spain under contract TIN2011-28689-C02- 02.ca_ES
dc.language.isoengca_ES
dc.publisherSpringer Verlagca_ES
dc.relationMICINN/PN2008-2011/TIN2011-28689-C02ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1007/s10586-013-0310-7ca_ES
dc.relation.ispartofCluster Computing, 2014, vol. 17, p. 271-279ca_ES
dc.rights(c) Springer Verlag, 2014ca_ES
dc.subjectCluster computingca_ES
dc.subjectEcosystem simulationca_ES
dc.subjectParameter calibrationca_ES
dc.titlePSysCal: a parallel tool for calibration of ecosystem modelsca_ES
dc.typearticleca_ES
dc.identifier.idgrec020466
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_ES
dc.identifier.doihttps://doi.org/10.1007/s10586-013-0310-7
dc.date.embargoEndDate2025-01-01


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