Analysis of structures by total potential optimization using meta-heuristic algorithms (TPO/MA)

Kitap BölümüYalnız künye

Özet

Use of meta-heuristic algorithms in analysis of structures is much more recent than their use in design of structures. Effectively, while design optimization makes use of genetic algorithms, simulated annealing, swarm optimization, etc. since about half a century with an increasing interest, these techniques are being used in structural analysis since a decade only. Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA), though being an emerging method at the moment, has proved to be very efficient in solving nonlinear problems, involving both material and geometric nonlinearities. The capabilities of the method in treating under-constrained problems and problems with non-unique solutions are much more advanced than the classical methods including the well-known Finite Element Method.

RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/bayburt/12221Resmi atıflarda kullanılan kalıcı adres
İndeks kayıtları
Kaydın indeks veri tabanlarındaki sayfası (aktarımda alınan kayıt numarasıyla). Erişim, indeksin kendi koşullarına bağlıdır.
Atıflar
Scopus
0
Her kaynağın kendi saydığı toplam atıf; aktarım ve zenginleştirme sırasında alındı.
Tam Kayıt
AlanDeğerDil
dc.contributor.authorToklu, Y.C.
dc.contributor.authorToklu, N.E.
dc.date.accessioned2026-10-29T19:09:20.181389218Z
dc.date.available2026-10-29T19:09:20.181389218Z
dc.date.issued2017
dc.description.abstractUse of meta-heuristic algorithms in analysis of structures is much more recent than their use in design of structures. Effectively, while design optimization makes use of genetic algorithms, simulated annealing, swarm optimization, etc. since about half a century with an increasing interest, these techniques are being used in structural analysis since a decade only. Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA), though being an emerging method at the moment, has proved to be very efficient in solving nonlinear problems, involving both material and geometric nonlinearities. The capabilities of the method in treating under-constrained problems and problems with non-unique solutions are much more advanced than the classical methods including the well-known Finite Element Method.
dc.identifier.isbn978-153612200-8
dc.identifier.isbn978-153612022-6
dc.identifier.scopus2-s2.0-85034976089
dc.identifier.startpage31
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/bayburt/12221
dc.identifier.volume2
dc.language.isoen
dc.publisherNova Science Publishers, Inc.
dc.relation.ispartofMathematical Research Summaries
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subject.otherGenetic algorithms
dc.subject.otherHeuristic methods
dc.subject.otherProblem solving
dc.subject.otherShape optimization
dc.subject.otherSimulated annealing
dc.subject.otherClassical methods
dc.subject.otherDesign optimization
dc.subject.otherGeometric non-linearity
dc.subject.otherMeta heuristic algorithm
dc.subject.otherNon-unique solutions
dc.subject.otherNonlinear problems
dc.subject.otherSwarm optimization
dc.subject.otherUnder-constrained
dc.subject.otherHeuristic algorithms
dc.titleAnalysis of structures by total potential optimization using meta-heuristic algorithms (TPO/MA)
dc.typeBook Chapter
rdl.citation.scopus0
rdl.oa.statusclosed
rdl.source.indexSCOPUS