Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

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dc.contributor.authorGonzalez-Pardo, Antonio
dc.contributor.authorDel Ser, Javier
dc.contributor.authorCamacho, David
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:07:27Z
dc.date.available2024-07-24T12:07:27Z
dc.date.issued2017-11
dc.descriptionPublisher Copyright: © 2017 Elsevier B.V.
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
dc.description.statusPeer reviewed
dc.format.extent15
dc.identifier.citationGonzalez-Pardo , A , Del Ser , J & Camacho , D 2017 , ' Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems ' , Applied Soft Computing Journal , vol. 60 , pp. 241-255 . https://doi.org/10.1016/j.asoc.2017.06.042
dc.identifier.doi10.1016/j.asoc.2017.06.042
dc.identifier.issn1568-4946
dc.identifier.urihttps://hdl.handle.net/11556/3769
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85022033961&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Soft Computing Journal
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsAnt Colony Optimization
dc.subject.keywordsConstraint Satisfaction Problems
dc.subject.keywordsCoral Reef Optimization
dc.subject.keywordsOblivion Rate
dc.subject.keywordsPheromone control
dc.subject.keywordsProject Scheduling Problems
dc.subject.keywordsSoftware
dc.titleComparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problemsen
dc.typejournal article
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