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Towards Optimal Algorithmic Parameters for Simulation-Based Multi-Objective Optimization
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
2016 (English)In: 2016 IEEE Congress on Evolutionary Computation (CEC), New York: IEEE, 2016, 5162-5169 p.Conference paper (Refereed)
Abstract [en]

The use of optimization to solve a simulation-based multi-objective problem produces a set of solutions that provide information about the trade-offs that have to be considered by the decision maker. An incomplete or sub-optimal set of solutions will negatively affect the quality of any subsequent decisions. The parameters that control the search behavior of an optimization algorithm can be used to minimize this risk. However, choosing good parameter settings for a given optimization algorithm and problem combination is difficult. The aim of this paper is to take a step towards optimal parameter settings for optimization of simulation-based problems. Two parameter tuning methods, Latin Hypercube Sampling and Genetic Algorithms, are used to maximize the performance of NSGA-II applied to a simulation-based problem with discrete variables. The strengths and weaknesses of both methods are analyzed. The effect of the number of decision variables and the function budget on the optimal parameter settings is also studied.

Place, publisher, year, edition, pages
New York: IEEE, 2016. 5162-5169 p.
Series
IEEE Congress on Evolutionary Computation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:his:diva-13331DOI: 10.1109/CEC.2016.7748344ISI: 000390749105045ScopusID: 2-s2.0-85008258262ISBN: 978-1-5090-0623-6 (electronic)ISBN: 978-1-5090-0622-9 ISBN: 978-1-5090-0624-3 OAI: oai:DiVA.org:his-13331DiVA: diva2:1067397
Conference
2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2017-01-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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Output format
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