Harnessing Particle Swarm optimization through Relativistic Velocity
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/CEC48606.2020.9185752 http://hdl.handle.net/11449/208018 |
Resumo: | In 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. |
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Harnessing Particle Swarm optimization through Relativistic VelocityGlobal optimizationMeta-Heuristic optimizationParticle Swarm optimizationRelativistic Particle Swarm optimizationTheory of RelativityIn 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.UNESP-São Paulo State University Department of ComputingUNESP-São Paulo State University Department of ComputingUniversidade Estadual Paulista (Unesp)Roder, Mateus [UNESP]De Rosa, Gustavo Henrique [UNESP]Passos, Leandro Aparecido [UNESP]Papa, Joao Paulo [UNESP]Rossi, Andre Luis Debiaso [UNESP]2021-06-25T11:05:00Z2021-06-25T11:05:00Z2020-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/CEC48606.2020.91857522020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings.http://hdl.handle.net/11449/20801810.1109/CEC48606.2020.91857522-s2.0-85092031031Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedingsinfo:eu-repo/semantics/openAccess2024-04-23T16:11:12Zoai:repositorio.unesp.br:11449/208018Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Harnessing Particle Swarm optimization through Relativistic Velocity |
title |
Harnessing Particle Swarm optimization through Relativistic Velocity |
spellingShingle |
Harnessing Particle Swarm optimization through Relativistic Velocity Roder, Mateus [UNESP] Global optimization Meta-Heuristic optimization Particle Swarm optimization Relativistic Particle Swarm optimization Theory of Relativity |
title_short |
Harnessing Particle Swarm optimization through Relativistic Velocity |
title_full |
Harnessing Particle Swarm optimization through Relativistic Velocity |
title_fullStr |
Harnessing Particle Swarm optimization through Relativistic Velocity |
title_full_unstemmed |
Harnessing Particle Swarm optimization through Relativistic Velocity |
title_sort |
Harnessing Particle Swarm optimization through Relativistic Velocity |
author |
Roder, Mateus [UNESP] |
author_facet |
Roder, Mateus [UNESP] De Rosa, Gustavo Henrique [UNESP] Passos, Leandro Aparecido [UNESP] Papa, Joao Paulo [UNESP] Rossi, Andre Luis Debiaso [UNESP] |
author_role |
author |
author2 |
De Rosa, Gustavo Henrique [UNESP] Passos, Leandro Aparecido [UNESP] Papa, Joao Paulo [UNESP] Rossi, Andre Luis Debiaso [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Roder, Mateus [UNESP] De Rosa, Gustavo Henrique [UNESP] Passos, Leandro Aparecido [UNESP] Papa, Joao Paulo [UNESP] Rossi, Andre Luis Debiaso [UNESP] |
dc.subject.por.fl_str_mv |
Global optimization Meta-Heuristic optimization Particle Swarm optimization Relativistic Particle Swarm optimization Theory of Relativity |
topic |
Global optimization Meta-Heuristic optimization Particle Swarm optimization Relativistic Particle Swarm optimization Theory of Relativity |
description |
In 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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-01 2021-06-25T11:05:00Z 2021-06-25T11:05:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/CEC48606.2020.9185752 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. http://hdl.handle.net/11449/208018 10.1109/CEC48606.2020.9185752 2-s2.0-85092031031 |
url |
http://dx.doi.org/10.1109/CEC48606.2020.9185752 http://hdl.handle.net/11449/208018 |
identifier_str_mv |
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. 10.1109/CEC48606.2020.9185752 2-s2.0-85092031031 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1797789388199952384 |