An adaptive local search with prioritized tracking for Dynamic Environments

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dc.contributor.authorMasegosa Arredondo, Antonio David
dc.contributor.authorOnieva Caracuel, Enrique
dc.contributor.authorLópez García, Pedro
dc.contributor.authorOsaba, Eneko
dc.contributor.authorPerallos Ruiz, Asier
dc.date.accessioned2026-03-02T11:06:03Z
dc.date.available2026-03-02T11:06:03Z
dc.date.issued2015-12-01
dc.date.updated2026-03-02T11:06:03Z
dc.description.abstractDynamic Optimization Problems (DOPs) have attracted a growing interest in recent years. This interest is mainly due to two reasons: their closeness to practical real conditions and their high complexity. The majority of the approaches proposed so far to solve DOPs are population-based methods, because it is usually believed that their higher diversity allows a better detection and tracking of changes. However, recent studies have shown that trajectory-based methods can also provide competitive results. This work is focused on this last type of algorithms. Concretely, it proposes a new adaptive local search for continuous DOPs that incorporates a memory archive. The main novelties of the proposal are two-fold: the prioritized tracking, a method to determine which solutions in the memory archive should be tracked first; and an adaptive mechanism to control the minimum step-length or precision of the search. The experimentation done over the Moving Peaks Problem (MPB) shows the benefits of the prioritized tracking and the adaptive precision mechanism. Furthermore, our proposal obtains competitive results with respect to state-of-the-art algorithms for the MPB, both in terms of performance and tracking ability.en
dc.description.sponsorshipThis work has been supported by the research projects TEC2013-45585-C2-2-R and TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness, and PC2013-71A from the Basque Governmenten
dc.identifier.citationMasegosa, Onieva, Lopez-Garcia, Osaba, & Perallos. (2015). An adaptive local search with prioritized tracking for Dynamic Environments. International Journal of Computational Intelligence Systems, 8(6), 1053-1075. https://doi.org/10.1080/18756891.2015.1113736
dc.identifier.doi10.1080/18756891.2015.1113736
dc.identifier.eissn1875-6883
dc.identifier.issn1875-6891
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5294
dc.language.isoeng
dc.publisherSpringer Science and Business Media B.V.
dc.rightsCopyright: the authors
dc.subject.otherAdaptive metaheuristics
dc.subject.otherDynamic Environments
dc.subject.otherDynamic Optimization Problems
dc.subject.otherLocal search
dc.subject.otherPrioritized tracking
dc.subject.otherTrajectory-based methods
dc.titleAn adaptive local search with prioritized tracking for Dynamic Environmentsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage1075
oaire.citation.issue6
oaire.citation.startPage1053
oaire.citation.titleInternational Journal of Computational Intelligence Systems
oaire.citation.volume8
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc/4.0/
oaire.versionVoR
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