Local search with quadratic approximation in genetic algorithms for expensive optimization problems

Elizabeth F. Wanner*, Frederico G. Guimaraes, Ricardo H.C. Takahashi, Peter J. Fleming

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

In this paper, we propose a local search methodology to be coupled with a Genetic Algorithm to solve optimization problems with non-linear constraints. This methodology uses quadratic approximations for both objective function and constraints. In the local search phase, these quadratic approximations define an associated problem that is solved using a linear matrix inequality (LMI) formulation. The number of function evaluations needed for finding the point of optimum is significantly reduced with this procedure, what makes the proposed methodology suitable for dealing with costly blackbox optimization problems. A case study is presented: the well-known TEAM 22 benchmark problem, an expensive problem of electromagnetic design. The results show that the hybrid algorithm has a better performance when compared to the same Genetic Algorithm without the proposed local search operator.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
PublisherIEEE
Pages677-683
Number of pages7
ISBN (Print)1424413400, 9781424413409
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sep 200728 Sep 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period25/09/0728/09/07

Fingerprint Dive into the research topics of 'Local search with quadratic approximation in genetic algorithms for expensive optimization problems'. Together they form a unique fingerprint.

Cite this