Implementation of a One-Stage Efficient Global Optimization (EGO) Algorithm
2009 (English)Report (Other academic)
Almost every Costly Global Optimization (CGO) solver utilizes a surrogate model, or response surface, to approximate the true (costly) function. The EGO algorithm introduced by Jones et al. utilizes the DACE framework to build an approximating surrogate model. By optimizing a less costly utility function, the algorithm determines a new point where the original objective function is evaluated. This is repeated until some convergence criteria is fulfilled.The original EGO algorithm finds the new point to sample in a two-stage process. In its first stage, the estimates of the interpolation parameters are optimized with respect to already sampled points. In the second stage, these estimated values are considered true in order to optimize the location of the new point. The use of estimate values as correct introduces a source of error.Instead, in the one-stage EGO algorithm, both the parameters and the location of a new point are optimized at the same time, removing the source of error. This new subproblem becomes more difficult, but eliminates the need of solving two subproblems.Difficulties in implementing a fast and robust One-Stage EGO algorithm in TOMLAB are discussed, especially the solution of the new subproblem.
Place, publisher, year, edition, pages
2009. , 26 p.
, Research Report 2009, School of Education, Culture and Communication, Division of Applied Mathematics, Mälardalen University, ISSN 1404-4978 ; 2009:2
Global Optimization, Costly, EGO
IdentifiersURN: urn:nbn:se:liu:diva-77075OAI: oai:DiVA.org:liu-77075DiVA: diva2:524834