Multilevel optimization strategies based on metamodel-assisted evolutionary algorithms, for computationally expensive problems

Kampolis, IC; Zyinaris, AS; Asouti, VG; Giannakoglou, KC

dc.contributor.author	Kampolis, IC	en
dc.contributor.author	Zyinaris, AS	en
dc.contributor.author	Asouti, VG	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T02:44:50Z
dc.date.available	2014-03-01T02:44:50Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31972
dc.subject	Adjoint Method	en
dc.subject	Boundary Layer	en
dc.subject	Coarse Grained	en
dc.subject	Difference Set	en
dc.subject	Evolutionary Algorithm	en
dc.subject	Multiprocessor Systems	en
dc.subject	Objective Function	en
dc.subject	Optimal Method	en
dc.subject	Radial Basis Function Network	en
dc.subject	Search Engine	en
dc.subject	Shape Optimization	en
dc.subject	evolutionary algo rithm	en
dc.subject	Gradient Descent	en
dc.subject	navier stokes	en
dc.subject.other	Aforementioned techniques	en
dc.subject.other	Airfoil parameterizations	en
dc.subject.other	Multilevel optimization	en
dc.subject.other	Metadata	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Search engines	en
dc.subject.other	Set theory	en
dc.subject.other	Evolutionary algorithms	en
dc.title	Multilevel optimization strategies based on metamodel-assisted evolutionary algorithms, for computationally expensive problems	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CEC.2007.4425008	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CEC.2007.4425008	en
heal.identifier.secondary	4425008	en
heal.publicationDate	2007	en
heal.abstract	In this paper, three multilevel optimization strategies are presented and applied to the design of isolated and cascade airfoils. They are all based on the same general-purpose search platform, which employs Hierarchical, Distributed Metamodel - Assisted Evolutionary Algorithms (HDMAEAs). The core search engine is an Evolutionary Algorithm (EA) assisted by local metamodels (radial basis function networks) which, for each population member, are trained anew on a ""suitable"" subset of the already evaluated solutions. The hierarchical scheme has a two - level structure, although it may accommodate any number of levels. At each level, the user may link (a) a different evaluation tool, such as low or high fidelity discipline - specific software, (b) a different optimization method, selected amongst stochastic and deterministic algorithms and/or (c) a different set of design variables, according to coarse and fine problem parameterizations. In the aerodynamic shape optimization problems presented in this paper, the three aforementioned techniques resort on (a) Navier - Stokes and integral boundary layer solvers, (b) evolutionary and gradient-descent algorithms where the adjoint method computes the objective function gradient and (c) airfoil parameterizations with different numbers of Bézier control points. The EAs used at any level are coarse - grained distributed EAs with a different MAEA at each deme. The three variants of the HDMAEA can be used either separately or in combination, in order to reduce the CPU cost. The optimization software runs in parallel, on multiprocessor systems. ©2007 IEEE.	en
heal.journalName	2007 IEEE Congress on Evolutionary Computation, CEC 2007	en
dc.identifier.doi	10.1109/CEC.2007.4425008	en
dc.identifier.spage	4116	en
dc.identifier.epage	4123	en