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Generating Uniformly Distributed Points on a Unit Simplex for Evolutionary Many-Objective Optimization
Michigan State University, East Lansing, USA.ORCID iD: 0000-0001-7402-9939
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0001-5436-2128
Ford Motor Company, Dearborn, USA.
2019 (English)In: Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings / [ed] Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed, Cham, Switzerland: Springer, 2019, Vol. 11411, p. 179-190Conference paper, Published paper (Refereed)
Abstract [en]

Most of the recently proposed evolutionary many-objective optimization (EMO) algorithms start with a number of predefined reference points on a unit simplex. These algorithms use reference points to create reference directions in the original objective space and attempt to find a single representative near Pareto-optimal point around each direction. So far, most studies have used Das and Dennis’s structured approach for generating a uniformly distributed set of reference points on the unit simplex. Due to the highly structured nature of the procedure, this method does not scale well with an increasing number of objectives. In higher dimensions, most created points lie on the boundary of the unit simplex except for a few interior exceptions. Although a level-wise implementation of Das and Dennis’s approach has been suggested, EMO researchers always felt the need for a more generic approach in which any arbitrary number of uniformly distributed reference points can be created easily at the start of an EMO run. In this paper, we discuss a number of methods for generating such points and demonstrate their ability to distribute points uniformly in 3 to 15-dimensional objective spaces.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2019. Vol. 11411, p. 179-190
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Keywords [en]
Many-objective optimization, Reference points, Das and Dennis points, Diversity preservation
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16713DOI: 10.1007/978-3-030-12598-1_15Scopus ID: 2-s2.0-85063041223ISBN: 978-3-030-12597-4 (print)ISBN: 978-3-030-12598-1 (electronic)OAI: oai:DiVA.org:his-16713DiVA, id: diva2:1298650
Conference
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Projects
Knowledge-Driven Decision Support (KDDS)
Funder
Knowledge Foundation, 41231
Note

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11411)

Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-05-23Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2020-02-03 00:00
Available from 2020-02-03 00:00

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Publisher's full textScopus

Authority records BETA

Deb, KalyanmoyBandaru, Sunith

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