Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control

S. D'Angelo; E.A. Minisci

doi:10.1109/CEC.2005.1554835

Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control

S. D'Angelo, E.A. Minisci

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

11 Citations (Scopus)

Abstract

This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.

Original language	English
Title of host publication	2005 IEEE Congress on Evolutionary Computation - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1262-1267
Number of pages	6
ISBN (Print)	0780393635
DOIs	https://doi.org/10.1109/CEC.2005.1554835
Publication status	Published - 12 Dec 2005
Event	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom Duration: 2 Sep 2005 → 5 Sep 2005

Conference

Conference	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
Country/Territory	United Kingdom
City	Edinburgh, Scotland
Period	2/09/05 → 5/09/05

Keywords

optimization
kriging approximation
aerodynamics

Access to Document

10.1109/CEC.2005.1554835

Cite this

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title = "Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control",

abstract = "This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.",

keywords = "optimization, kriging approximation, aerodynamics",

author = "S. D'Angelo and E.A. Minisci",

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doi = "10.1109/CEC.2005.1554835",

language = "English",

isbn = "0780393635",

pages = "1262--1267",

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note = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",

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D'Angelo, S & Minisci, EA 2005, Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control. in 2005 IEEE Congress on Evolutionary Computation - Proceedings. IEEE, Piscataway, NJ, pp. 1262-1267, 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Edinburgh, Scotland, United Kingdom, 2/09/05. https://doi.org/10.1109/CEC.2005.1554835

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N2 - This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.

AB - This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.

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T2 - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005

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ER -

Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control

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