Hybrid meta-heuristic algorithm for the simultaneous optimization of the O-D trip matrix estimation

Stathopoulos, A; Tsekeris, T

dc.contributor.author	Stathopoulos, A	en
dc.contributor.author	Tsekeris, T	en
dc.date.accessioned	2014-03-01T01:20:35Z
dc.date.available	2014-03-01T01:20:35Z
dc.date.issued	2004	en
dc.identifier.issn	1093-9687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15976
dc.subject	Heuristic Algorithm	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Computer aided design	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Heuristic methods	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Convergence rates	en
dc.subject.other	Meta-heuristic optimization algorithms	en
dc.subject.other	Urban road networks	en
dc.subject.other	Civil engineering	en
dc.subject.other	civil engineering	en
dc.subject.other	genetic algorithm	en
dc.title	Hybrid meta-heuristic algorithm for the simultaneous optimization of the O-D trip matrix estimation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1467-8667.2004.00367.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1467-8667.2004.00367.x	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	In the present article, the origin-destination (O-D) trip matrix estimation is formulated as a simultaneous optimization problem and is resolved by employing three different meta-heuristic optimization algorithms. These include a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a hybrid algorithm (GASA) based on the combination of GA and SA. The computational performance of the three algorithms is evaluated and compared by implementing them on a realistic urban road network. The results of the simulation tests demonstrate that SA and GASA produce a more accurate final solution than GA, whereas GASA shows a superior convergence rate, that is, faster improvement from the initial solution, in comparison to SA and GA. In addition, GASA produces a final solution that is more robust and less dependent on the initial demand pattern, in comparison to that obtained from a greedy search algorithm. © 2004 Computer-Aided Civil and Infrastructure Engineering.	en
heal.publisher	BLACKWELL PUBLISHERS	en
heal.journalName	Computer-Aided Civil and Infrastructure Engineering	en
dc.identifier.doi	10.1111/j.1467-8667.2004.00367.x	en
dc.identifier.isi	ISI:000223310800003	en
dc.identifier.volume	19	en
dc.identifier.issue	6	en
dc.identifier.spage	421	en
dc.identifier.epage	435	en