Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization

Liakopoulos, PIK; Kampolis, IC; Giannakoglou, KC

dc.contributor.author	Liakopoulos, PIK	en
dc.contributor.author	Kampolis, IC	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:28:31Z
dc.date.available	2014-03-01T01:28:31Z
dc.date.issued	2008	en
dc.identifier.issn	0167-739X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18869
dc.subject	Distributed search	en
dc.subject	Evolutionary algorithms	en
dc.subject	Grid computing	en
dc.subject	Hierarchical search	en
dc.subject	Metamodels	en
dc.subject	Optimization	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Aerodynamics	en
dc.subject.other	Authentication	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Grid computing	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Resource allocation	en
dc.subject.other	Distributed search	en
dc.subject.other	Hierarchical and Distributed scheme (HDMAEA)	en
dc.subject.other	Hierarchical search	en
dc.subject.other	Metamodels	en
dc.subject.other	Evolutionary algorithms	en
dc.title	Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.future.2008.03.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.future.2008.03.004	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	A Grid-enabled optimization environment is presented. It is based on Metamodel-Assisted Evolutionary Algorithms (MAEAs), where radial basis function networks, trained on the fly on selected subsets of the previously evaluated individuals, are used to pre-evaluate the population members. The search follows a Hierarchical and Distributed scheme (HDMAEA), with more than one search level, each of which is associated with a different problem-specific evaluation tool and a different number of semi-isolated demes. Irrespective of the use of cluster or Grid computing, the HDMAEA drastically reduces the number of evaluations required to reach the optimal solution(s). The Grid-enabled HDMAEA, based on the master-slave model with simultaneously evaluated population members, aims at solving large scale optimization problems in affordable wall clock time. In the proposed Grid-computing setup, Condor is used as the local resource manager on each contributing cluster, authentication and interfacing is carried out via the Globus Toolkit and the unification of Grid resources under a common queue is undertaken by the Gridway metascheduler. If more than one search level are used (hierarchical search), the optimization of Grid resources' allocation relies on the distinction between computationally demanding, high-accuracy and less demanding, low-accuracy evaluation tools. The proposed Grid-enabled problem solving environment is demonstrated on three aerodynamic shape optimization problems, namely the design of a compressor cascade and two 3D elbow ducts, on three remote clusters. (C) 2008 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Future Generation Computer Systems	en
dc.identifier.doi	10.1016/j.future.2008.03.004	en
dc.identifier.isi	ISI:000257354200008	en
dc.identifier.volume	24	en
dc.identifier.issue	7	en
dc.identifier.spage	701	en
dc.identifier.epage	708	en