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A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Production and automation engineering, Simulation-based Optimization)ORCID iD: 0000-0003-3432-5068
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Production and automation engineering, Simulation-based Optimization)ORCID iD: 0000-0003-0111-1776
Department of Electrical and Computer Engineering, Michigan State University, USA.ORCID iD: 0000-0001-7402-9939
2017 (English)In: Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings / [ed] Heike Trautmann, Rudolph Günter, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, and Christian Grimme, Springer, 2017, Vol. 10173, 560-574 p.Conference 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 with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II). Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged closeto the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front.In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems ofdifferent complexity and a simplistic production line model.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10173, 560-574 p.
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 10173
Keyword [en]
Evolutionary multi-objective optimization, Guided search, Preference-guided EMO, Reference point, Decision support, Adaptive
National Category
Computer Science
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-13448DOI: 10.1007/978-3-319-54157-0_38Scopus ID: 2-s2.0-85014258475ISBN: 978-3-319-54156-3 (print)ISBN: 978-3-319-54157-0 (electronic)OAI: oai:DiVA.org:his-13448DiVA: diva2:1084245
Conference
9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017
Funder
Knowledge Foundation
Available from: 2017-03-24 Created: 2017-03-24 Last updated: 2017-05-23Bibliographically approved

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CiteExportLink to record
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