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An empirical comparison of metamodeling strategies in noisy environments
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0003-0111-1776
2018 (English)In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018) / [ed] Hernan Aguirre, New York, NY, USA: ACM Digital Library, 2018, p. 817-824, article id 3205509Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2018. p. 817-824, article id 3205509
Series
GECCO '18
Keywords [en]
simulation, optimization, metamodeling, noise
National Category
Computer Sciences Other Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-15966DOI: 10.1145/3205455.3205509Scopus ID: 2-s2.0-85050638821ISBN: 978-1-4503-5618-3 (electronic)OAI: oai:DiVA.org:his-15966DiVA, id: diva2:1232570
Conference
Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018
Projects
Synergy KDDS
Funder
Knowledge Foundation, 41231Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2019-03-27

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Bandaru, SunithNg, Amos H. C.

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Citation style
  • apa
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  • modern-language-association-8th-edition
  • vancouver
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More styles
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