Evolutionary computation and swarm intelligence algorithms in structural optimization

Abdalghaffar, Emad

dc.contributor.author	Abdalghaffar, Emad	el
dc.date.accessioned	2022-02-18T15:59:41Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54771
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.22469
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/gr/	*
dc.subject	Structural optimization	en
dc.subject	Metaheuristic optimization algorithms	en
dc.subject	Neural networks	en
dc.subject	Machine learning	en
dc.subject	Evolutionary optimization	en
dc.subject	Swarm intelligence optimization	en
dc.title	Evolutionary computation and swarm intelligence algorithms in structural optimization	en
heal.type	masterThesis
heal.generalDescription	Optimization is the heart of many natural processes, to name a few: the natural selection phenomenon in the biological evolution theory that is based on the survival-of-the-fittest principle, the social swarming behavior of birds or the foraging strategies of ants. Hence, the concept of simulating such natural phenomena into computational mechanics in form of computer algorithms may offer a very promising approach in the nowadays computer-aided engineering designs.	en
heal.classification	Structural Optimization	en
heal.dateAvailable	2023-02-17T22:00:00Z
heal.language	en
heal.access	embargo
heal.recordProvider	ntua	el
heal.publicationDate	2021-07-07
heal.abstract	This Pharaonic-Grecian work aims to frame the art of the evolutionary and swarm based structural optimization. That is through five chapters. At the first chapter, the structural optimization problem is properly defined as well to elaborating its basic components. Then, an intensive review of the literature is introduced, through screening the most recent and most cited scientific articles in the topic during the first two decades of the 21st century. Afterwards, a set of the most promising and trending state-of-the-art algorithms is introduced through descriptive paragraphs that elaborate how each algorithm works. Following such elaboration of the literature and the state of the art, numerical tests assessing the performance of these most promising algorithms have been done in order to identify the capability of each algorithm, and eventually estimating the future trends in Structural Optimization.	en
heal.advisorName	Lagaros, Nikos	en
heal.advisorName	Λαγαρός, Νικόλαος	el
heal.committeeMemberName	Koumousis, Vlasis	en
heal.committeeMemberName	Κουμούσης, Βλάσιος	el
heal.committeeMemberName	Triantafyllou, Savvas	en
heal.committeeMemberName	Τριανταφύλλου, Σάββας	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Πολιτικών Μηχανικών. Τομέας Δομοστατικής. Εργαστήριο Στατικής και Αντισεισμικών Ερευνών	el
heal.academicPublisherID	ntua
heal.numberOfPages	62 σ.	el
heal.fullTextAvailability	false