dc.creator | García Nieto, José Manuel | es |
dc.creator | Alba, Enrique | es |
dc.date.accessioned | 2021-05-07T10:47:33Z | |
dc.date.available | 2021-05-07T10:47:33Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | García Nieto, J.M. y Alba, E. (2015). Hybrid PSO6 for Hard Continuous Optimization. Soft Computing, 19, 1843-1861. | |
dc.identifier.issn | 1432-7643 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108703 | |
dc.description.abstract | In our previous works, we empirically showed
that a number of 6±2 informants may endow particle swarm
optimization (PSO) with an optimized learning procedure in
comparison with other combinations of informants. In this way,
the new version PSO6, that evolves new particles from six
informants (neighbors), performs more accurately that other
existing versions of PSO and is able to generate good particles for
a longer time. Despite this advantage, PSO6 may show certain
attraction to local basins derived from its moderate performance
on non-separable complex problems (typically observed in PSO
versions). In this paper, we incorporate a local search procedure
to the PSO6 with the aim of correcting this disadvantage. We
compare the performance of our proposal (PSO6-Mtsls) on a set
of 40 benchmark functions against that of other PSO versions,
as well as against the best recent proposals in the current
state of the art (with and without local search). The results
support our conjecture that the (quasi)-optimally informed PSO,
hybridized with local search mechanisms, reaches a high rate of
success on a large number of complex (non-separable) continuous
optimization functions. | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-03044 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2011-28194 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación BES-2009-018767 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Soft Computing, 19, 1843-1861. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Particle Swarm Optimization | es |
dc.subject | Fully Informed PSO | es |
dc.subject | Multiple Trajectory Search | es |
dc.subject | Benchmarking Functions | es |
dc.title | Hybrid PSO6 for Hard Continuous Optimization | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | P07-TIC-03044 | es |
dc.relation.projectID | TIN2011-28194 | es |
dc.relation.projectID | BES-2009-018767 | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s00500-014-1368-8 | es |
dc.identifier.doi | 10.1007/s00500-014-1368-8 | es |
dc.journaltitle | Soft Computing | es |
dc.publication.volumen | 19 | es |
dc.publication.initialPage | 1843 | es |
dc.publication.endPage | 1861 | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |