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dc.contributor.authorBali, Olfa
dc.contributor.authorElloumi, Walid
dc.contributor.authorAbraham, Ajith
dc.contributor.authorAlimi, Adel M.
dc.date.accessioned2017-02-01T10:01:36Z
dc.date.available2017-02-01T10:01:36Z
dc.date.issued2016
dc.identifier.citationJournal of Information Assurance and Security. 2016, vol. 11, issue 6, p. 369-384.cs
dc.identifier.issn1554-1010
dc.identifier.issn1554-1029
dc.identifier.urihttp://hdl.handle.net/10084/116838
dc.description.abstractThe Travelling Salesman Problem (TSP) is a well-known benchmark problem for many meta-heuristic algorithms, including security traffic optimization problems. TSP is known as NP hard complex. It was investigated using classical approaches as well as intelligent techniques using Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and other meta-heuristics. The Graphic Processing Units (GPU) is well suited to the execution of nature and bio-inspired algorithms due to the rapidity of parallel implementation of GPUs. In this paper, we present a novel parallel approach to run PSO and ACO on GPUs and applied to TSP (GPU-PSO&ACO-A-TSP) for security tracking vehicles in road traffic. Both algorithms are implemented on the GPUs. Results show better performance optimization when using GPUs compared to results using sequential CPU implementation.cs
dc.language.isoencs
dc.publisherDynamic Publisherscs
dc.relation.ispartofseriesJournal of Information Assurance and Securitycs
dc.subjectPSOcs
dc.subjectACOcs
dc.subjectTSPcs
dc.subjectGPUcs
dc.subjectCUDAcs
dc.subjectoptimizationcs
dc.subjectsecuritycs
dc.titleGPU PSO and ACO applied to TSP for vehicle security trackingcs
dc.typearticlecs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume11cs
dc.description.issue6cs
dc.description.lastpage384cs
dc.description.firstpage369cs
dc.identifier.wos000391049000007


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