Analysis of structures by total potential optimization using meta-heuristic algorithms (TPO/MA)
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.
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Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Toklu, Y.C. | |
| dc.contributor.author | Toklu, N.E. | |
| dc.date.accessioned | 2026-10-29T19:09:20.181389218Z | |
| dc.date.available | 2026-10-29T19:09:20.181389218Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | 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. | |
| dc.identifier.isbn | 978-153612200-8 | |
| dc.identifier.isbn | 978-153612022-6 | |
| dc.identifier.scopus | 2-s2.0-85034976089 | |
| dc.identifier.startpage | 31 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/bayburt/12221 | |
| dc.identifier.volume | 2 | |
| dc.language.iso | en | |
| dc.publisher | Nova Science Publishers, Inc. | |
| dc.relation.ispartof | Mathematical Research Summaries | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject.other | Genetic algorithms | |
| dc.subject.other | Heuristic methods | |
| dc.subject.other | Problem solving | |
| dc.subject.other | Shape optimization | |
| dc.subject.other | Simulated annealing | |
| dc.subject.other | Classical methods | |
| dc.subject.other | Design optimization | |
| dc.subject.other | Geometric non-linearity | |
| dc.subject.other | Meta heuristic algorithm | |
| dc.subject.other | Non-unique solutions | |
| dc.subject.other | Nonlinear problems | |
| dc.subject.other | Swarm optimization | |
| dc.subject.other | Under-constrained | |
| dc.subject.other | Heuristic algorithms | |
| dc.title | Analysis of structures by total potential optimization using meta-heuristic algorithms (TPO/MA) | |
| dc.type | Book Chapter | |
| rdl.citation.scopus | 0 | |
| rdl.oa.status | closed | |
| rdl.source.index | SCOPUS |
