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dc.contributor.authorGonzalez-Pardo, A.
dc.contributor.authorDel Ser, J. 
dc.contributor.authorCamacho, D.
dc.date.accessioned2017-07-25T17:17:20Z
dc.date.available2017-07-25T17:17:20Z
dc.date.issued2017-11
dc.identifier.urihttp://hdl.handle.net/20.500.11824/708
dc.description.abstractConstraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectOblivion Rateen_US
dc.subjectPheromone controlen_US
dc.subjectAnt Colony Optimizationen_US
dc.subjectProject Scheduling Problemsen_US
dc.subjectConstraint Satisfaction Problemsen_US
dc.subjectCoral Reef Optimizationen_US
dc.titleComparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problemsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.asoc.2017.06.042
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S156849461730385Xen_US
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleApplied Soft Computingen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España