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A Ranking and Selection Strategy for Preference-based Evolutionary Multi-objective Optimization of Variable-Noise Problems
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0003-3432-5068
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
Department of Electrical and Computer Engineering, Michigan State University, USA.ORCID iD: 0000-0001-7402-9939
2016 (English)In: 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE conference proceedings, 2016, 3035-3044 p.Conference paper, Published paper (Refereed)
Abstract [en]

In simulation-based Evolutionary Multi-objective Optimization the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space, for example with the R-NSGA-II algorithm [9], which uses a reference point specified by the decision maker. When stochastic systems are simulated, the uncertainty of the objective values might degrade the optimization performance. By sampling the solutions multiple times this uncertainty can be reduced. However, resampling methods reduce the overall number of evaluated solutions which potentially worsens the optimization result. In this article, a Dynamic Resampling strategy is proposed which identifies the solutions closest to the reference point which guides the population of the Evolutionary Algorithm. We apply a single-objective Ranking and Selection resampling algorithm in the selection step of R-NSGA-II, which considers the stochastic reference point distance and its variance to identify the best solutions. We propose and evaluate different ways to integrate the sampling allocation method into the Evolutionary Algorithm. On the one hand, the Dynamic Resampling algorithm is made adaptive to support the EA selection step, and it is customized to be used in the time-constrained optimization scenario. Furthermore, it is controlled by other resampling criteria, in the same way as other hybrid DR algorithms. On the other hand, R-NSGA-II is modified to rely more on the scalar reference point distance as fitness function. The results are evaluated on a benchmark problem with variable noise landscape.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 3035-3044 p.
Keyword [en]
Evolutionary, multi-objective optimization, preference-based, guided search, reference point, dynamic resampling, budget allocation, ranking and selection, variable noise
National Category
Information Systems Robotics
Research subject
Technology; Natural sciences
Identifiers
URN: urn:nbn:se:his:diva-13161DOI: 10.1109/CEC.2016.7744173ISI: 000390749103029Scopus ID: 2-s2.0-85008255213ISBN: 978-1-5090-0623-6 ISBN: 978-1-5090-0624-3 ISBN: 978-1-5090-0622-9 OAI: oai:DiVA.org:his-13161DiVA: diva2:1050928
Conference
2016 IEEE Congress on Evolutionary Computation (IEEE CEC) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCC) 2016, 24-29 July 2016, Vancouver, Canada
Funder
Knowledge Foundation
Available from: 2016-11-30 Created: 2016-11-30 Last updated: 2017-11-27Bibliographically approved

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Siegmund, FlorianNg, Amos H. C.Deb, Kalyanmoy

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CiteExportLink to record
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Citation style
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