dc.contributor.author | Bagheri, S | |
dc.contributor.author | Konen, W | |
dc.contributor.author | Allmendinger, R | |
dc.contributor.author | Branke, J | |
dc.contributor.author | Deb, K | |
dc.contributor.author | Fieldsend, JE | |
dc.contributor.author | Quagliarella, D | |
dc.contributor.author | Sindhya, K | |
dc.date.accessioned | 2017-04-19T13:06:34Z | |
dc.date.issued | 2017-07 | |
dc.description.abstract | Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes. In this work, we introduce a new EGO-based algorithm which tries to overcome these common issues with Kriging optimization algorithms. We apply the proposed algorithm on problems with dimension d ≤ 4 from the G-function suite [16] and on an airfoil shape example. | en_GB |
dc.description.sponsorship | This research was partly funded by Tekes, the Finnish Funding Agency for Innovation (the DeCoMo project), and by the Engineering and Physical Sciences Research Council [grant numbers EP/N017195/1, EP/N017846/1]. | en_GB |
dc.identifier.citation | GECCO 2017: Genetic and Evolutionary Computation Conference, 15-19 July 2017, Berlin, Germany | en_GB |
dc.identifier.doi | 10.1145/3071178.3071278 | |
dc.identifier.uri | http://hdl.handle.net/10871/27156 | |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computing Machinery (ACM) | en_GB |
dc.rights.embargoreason | Embargoed until after conference | en_GB |
dc.rights | © 2017 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. | en_GB |
dc.subject | Constraint optimization | en_GB |
dc.subject | expensive optimization | en_GB |
dc.subject | surrogate models | en_GB |
dc.subject | Kriging | en_GB |
dc.subject | Gaussian processes | en_GB |
dc.subject | EGO | en_GB |
dc.title | Constraint Handling in Efficient Global Optimization | en_GB |
dc.type | Conference paper | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from ACM via the DOI in this record. | en_GB |