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An empirical comparison of metamodeling strategies in noisy environments
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Simulation-Based Optimization)ORCID-id: 0000-0001-5436-2128
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Simulation-Based Optimization)ORCID-id: 0000-0003-0111-1776
2018 (engelsk)Inngår i: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018) / [ed] Hernan Aguirre, New York, NY, USA: ACM Digital Library, 2018, s. 817-824, artikkel-id 3205509Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.

sted, utgiver, år, opplag, sider
New York, NY, USA: ACM Digital Library, 2018. s. 817-824, artikkel-id 3205509
Serie
GECCO '18
Emneord [en]
simulation, optimization, metamodeling, noise
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-15966DOI: 10.1145/3205455.3205509Scopus ID: 2-s2.0-85050638821ISBN: 978-1-4503-5618-3 (digital)OAI: oai:DiVA.org:his-15966DiVA, id: diva2:1232570
Konferanse
Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018
Prosjekter
Synergy KDDS
Forskningsfinansiär
Knowledge Foundation, 41231Tilgjengelig fra: 2018-07-12 Laget: 2018-07-12 Sist oppdatert: 2019-03-27

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