Harnessing Particle Swarm optimization through Relativistic Velocity

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dc.contributor.authorRoder, Mateus [UNESP]
dc.contributor.authorDe Rosa, Gustavo Henrique [UNESP]
dc.contributor.authorPassos, Leandro Aparecido [UNESP]
dc.contributor.authorPapa, Joao Paulo [UNESP]
dc.contributor.authorRossi, Andre Luis Debiaso [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-06-25T11:05:00Z
dc.date.available2021-06-25T11:05:00Z
dc.date.issued2020-07-01
dc.description.abstractIn the last century, Albert Einstein's perceptions of the world afforded a revolution in the understanding of the universe. In his theory of general relativity, he describes the space-time continuum, a concept capable of explaining several phenomena, ranging from gravity to black holes and supernovas. Further, it also provides a set of formulations to generalize classical physics concepts to accommodate the relativistic notions. Meanwhile, several mathematicians have been working on optimization tools aiming to solve complex problems associated with a large number of variables. Nowadays, despite the computational power, many daily tasks still pose a challenge and are becoming more prohibitives, mostly due to the massive amount of data to be processed. Therefore, efficient optimization techniques are more desirable than ever. In this context, metaheuristic optimization has arisen, i.e., stochastic nature-inspired methods capable of finding sub-optimal solutions for complex problems with a reasonable computational effort. However, such approaches still suffer from some drawbacks related to low convergence and getting stuck on local optima, among others. Therefore, in this paper, we introduce relativistic concepts into the well-known meta-heuristic optimization technique Particle Swarm optimization (PSO). The experimental results evince the robustness of the proposed approach compared to the standard PSO as well as three other variations for five benchmarking functions.en
dc.description.affiliationUNESP-São Paulo State University Department of Computing
dc.description.affiliationUnespUNESP-São Paulo State University Department of Computing
dc.identifierhttp://dx.doi.org/10.1109/CEC48606.2020.9185752
dc.identifier.citation2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings.
dc.identifier.doi10.1109/CEC48606.2020.9185752
dc.identifier.scopus2-s2.0-85092031031
dc.identifier.urihttp://hdl.handle.net/11449/208018
dc.language.isoeng
dc.relation.ispartof2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
dc.sourceScopus
dc.subjectGlobal optimization
dc.subjectMeta-Heuristic optimization
dc.subjectParticle Swarm optimization
dc.subjectRelativistic Particle Swarm optimization
dc.subjectTheory of Relativity
dc.titleHarnessing Particle Swarm optimization through Relativistic Velocityen
dc.typeTrabalho apresentado em evento
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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Bauru - FC - Faculdade de Ciências