Premium
Derivative‐free optimization for expensive constrained problems using a novel expected improvement objective function
Author(s) -
Boukouvala Fani,
Ierapetritou Marianthi G.
Publication year - 2014
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.14442
Subject(s) - mathematical optimization , benchmark (surveying) , sampling (signal processing) , cluster analysis , computer science , kriging , local search (optimization) , feasible region , global optimization , trust region , mathematics , machine learning , computer security , geodesy , filter (signal processing) , radius , computer vision , geography
In this work, an algorithm for the optimization of costly constrained systems is introduced. The proposed method combines advantages of global‐ and local‐search algorithms with new concepts of feasibility space mapping, within a framework that aims to find global solutions with minimum sampling. A global search is initially performed, during which kriging surrogate models of the objective and the feasible region are developed. A novel search criterion for locating feasibility boundaries is introduced, which does not require any assumptions regarding the convexity and nonlinearity of the feasible space. Finally, local search is performed starting from multiple locations identified by clustering of previously obtained samples. The performance of the proposed approach is evaluated through both benchmark examples and a case study from the pharmaceutical industry. A comparison of the method with commercially available software reveals that the proposed method has a competitive performance in terms of sampling requirements and quality of solution. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2462–2474, 2014