An unstructured grid partitioning method based on genetic algorithms

Giotis, AP; Giannakoglou, KC

dc.contributor.author	Giotis, AP	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:13:34Z
dc.date.available	2014-03-01T01:13:34Z
dc.date.issued	1998	en
dc.identifier.issn	0965-9978	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12577
dc.subject	Cost Effectiveness	en
dc.subject	Cost Function	en
dc.subject	Distributed Memory	en
dc.subject	Genetic Algorithm	en
dc.subject	Genetic Operator	en
dc.subject	Graph Connectivity	en
dc.subject	Load Balance	en
dc.subject	Unstructured Grid	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Distributed computer systems	en
dc.subject.other	Graph theory	en
dc.subject.other	Parallel processing systems	en
dc.subject.other	Recursive functions	en
dc.subject.other	Lanczos algorithm	en
dc.subject.other	Recursive spectral bisection	en
dc.subject.other	Unstructured grid partitioning method	en
dc.subject.other	Genetic algorithms	en
dc.title	An unstructured grid partitioning method based on genetic algorithms	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0965-9978(98)00014-3	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0965-9978(98)00014-3	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	A cost-effective method for the recursive bisection of two-dimensional unstructured grids into 2(n) subdomains is introduced. The method is based on Genetic Algorithms (GAs) and is capable of generating evenly loaded disjoint mesh subsets with small interface length, that can be efficiently processed in parallel, on distributed memory platforms. Non-dimensional quotients, that express the load-balance and minimum communication requirements, are combined in a simple cost function controlled by the GA. The genetic operators are coupled with a single-pass multilevel scheme that allows for reduced size chromosomes to be processed. Upon convergence at any level, the GA continues to operate at the next finer grid using the same population of the uncoarsened current chromosomes. At the lowest level, a special refinement task, that permits local modifications only to the available partitions, is carried out. Crossover operators are standard, while a customised mutation operator, that takes into account the graph connectivity, is proposed. After completion of the mutation, a correction task enhances the convergence properties of the proposed method. (C) 1998 Published by Elsevier Science Limited. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Advances in Engineering Software	en
dc.identifier.doi	10.1016/S0965-9978(98)00014-3	en
dc.identifier.isi	ISI:000074944800006	en
dc.identifier.volume	29	en
dc.identifier.issue	2	en
dc.identifier.spage	129	en
dc.identifier.epage	138	en