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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
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Simulation-Based Optimization)ORCID-id: 0000-0001-5436-2128
Ford Motor Company, Dearborn, USA.
2019 (engelsk)Inngår i: 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, s. 179-190Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Cham, Switzerland: Springer, 2019. Vol. 11411, s. 179-190
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Emneord [en]
Many-objective optimization, Reference points, Das and Dennis points, Diversity preservation
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
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 (tryckt)ISBN: 978-3-030-12598-1 (digital)OAI: oai:DiVA.org:his-16713DiVA, id: diva2:1298650
Konferanse
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Prosjekter
Knowledge-Driven Decision Support (KDDS)
Forskningsfinansiär
Knowledge Foundation, 41231
Merknad

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

Tilgjengelig fra: 2019-03-25 Laget: 2019-03-25 Sist oppdatert: 2019-05-23bibliografisk kontrollert

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Fulltekst tilgjengelig fra 2020-02-03 00:00
Tilgjengelig fra 2020-02-03 00:00

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