Plant intelligence based metaheuristic optimization algorithms

MakaleAçık ErişimScopus SJRQ1
  • SDG 2 · Açlığa Son

Özet

Classical optimization algorithms are insufficient in large scale combinatorial problems and in nonlinear problems. Hence, metaheuristic optimization algorithms have been proposed. General purpose metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sport-based, mathematics-based, swarm-based, and hybrid methods which are combinations of these. Studies on plants in recent years have showed that plants exhibit intelligent behaviors. Accordingly, it is thought that plants have nervous system. In this work, all of the algorithms and applications about plant intelligence have been firstly collected and searched. Information is given about plant intelligence algorithms such as Flower Pollination Algorithm, Invasive Weed Optimization, Paddy Field Algorithm, Root Mass Optimization Algorithm, Artificial Plant Optimization Algorithm, Sapling Growing up Algorithm, Photosynthetic Algorithm, Plant Growth Optimization, Root Growth Algorithm, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree Optimization.

Anahtar Kelimeler

RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/munzur/4406Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s10462-016-9486-6Yayıncı resmi DOI adresi
İndeks kayıtları
Kaydın indeks veri tabanlarındaki sayfası (aktarımda alınan kayıt numarasıyla). Erişim, indeksin kendi koşullarına bağlıdır.
Atıflar
WoS
188
OpenAlex
218
Her kaynağın kendi saydığı toplam atıf; aktarım ve zenginleştirme sırasında alındı.
Tam Kayıt
AlanDeğerDil
dc.contributor.authorAkyol, Sinem
dc.contributor.authorAlatas, Bilal
dc.date.accessioned2026-10-28T14:59:16.800280095Z
dc.date.available2026-10-28T14:59:16.800280095Z
dc.date.issued2017-04
dc.description.abstractClassical optimization algorithms are insufficient in large scale combinatorial problems and in nonlinear problems. Hence, metaheuristic optimization algorithms have been proposed. General purpose metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sport-based, mathematics-based, swarm-based, and hybrid methods which are combinations of these. Studies on plants in recent years have showed that plants exhibit intelligent behaviors. Accordingly, it is thought that plants have nervous system. In this work, all of the algorithms and applications about plant intelligence have been firstly collected and searched. Information is given about plant intelligence algorithms such as Flower Pollination Algorithm, Invasive Weed Optimization, Paddy Field Algorithm, Root Mass Optimization Algorithm, Artificial Plant Optimization Algorithm, Sapling Growing up Algorithm, Photosynthetic Algorithm, Plant Growth Optimization, Root Growth Algorithm, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree Optimization.
dc.identifier.doi10.1007/s10462-016-9486-6
dc.identifier.eissn1573-7462
dc.identifier.endpage462
dc.identifier.issn0269-2821
dc.identifier.issue4
dc.identifier.startpage417
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/4406
dc.identifier.volume47
dc.identifier.wosWOS:000397506200001
dc.language.isoen
dc.publisherSPRINGER
dc.relation.ispartofARTIFICIAL INTELLIGENCE REVIEW
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectPlant intelligence
dc.subjectGlobal optimization
dc.subjectMetaheuristic methods
dc.subject.classificationComputer Science, Artificial Intelligence
dc.subject.classificationComputer Science
dc.subject.otherINVASIVE WEED OPTIMIZATION
dc.subject.otherIMPERIALIST COMPETITIVE ALGORITHM
dc.subject.otherFLOWER POLLINATION ALGORITHM
dc.subject.otherLEARNING-BASED OPTIMIZATION
dc.subject.otherSOLVE TOY MODEL
dc.subject.otherNONSMOOTH
dc.subject.otherDISPATCH
dc.subject.otherDESIGN
dc.titlePlant intelligence based metaheuristic optimization algorithms
dc.typeArticle
rdl.citation.openalex218
rdl.citation.wos188
rdl.oa.statuspreprint
rdl.oa.statusgreen
rdl.oa.urlhttps://hdl.handle.net/11508/61584
rdl.scopus.quartileQ1
rdl.sdgSDG02
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)