his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A two-step multi-objectivization method for improved evolutionary optimization of industrial 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: 0000-0003-3973-3394
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and automation engineering)
2018 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 64, p. 331-340Article in journal (Refereed) Published
Abstract [en]

Multi-objectivization means that helper objectives are added to an optimization problem with the purpose of altering the search space in a way that improves the progress of the optimization algorithm. In this paper, a new method for multi-objectivization is proposed that is based on a two-step process. In the first step, a helper objective that conflicts with the main objective is added, and in the second step a helper objective that is in harmony with, but subservient to, the main objective is added. In contrast to existing methods for multi-objectivization, the proposed method aims at obtaining improved results in real-world optimizations by focusing on three aspects: (a) adding as little extra complexity to the problem as possible, (b) achieving an optimal balance between exploration and exploitation in order to promote an efficient search, and (c) ensuring that the main objective, which is of main interest to the user, is always prioritized. Results from evaluating the proposed method on a complex real-world scheduling problem and a theoretical benchmark problem show that the method outperforms both a traditional single-objective approach and the prevailing method for multi-objectivization. Besides describing the proposed method, the paper also outlines interesting aspects of multi-objectivization to investigate in the future.

Place, publisher, year, edition, pages
2018. Vol. 64, p. 331-340
Keywords [en]
Multi-objectivization, Evolutionary algorithms, Optimization, Real-world scheduling problem
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-14733DOI: 10.1016/j.asoc.2017.12.027ISI: 000426011800023Scopus ID: 2-s2.0-85039695707OAI: oai:DiVA.org:his-14733DiVA, id: diva2:1181929
Funder
Knowledge FoundationAvailable from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-04-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Syberfeldt, Anna

Search in DiVA

By author/editor
Syberfeldt, Anna
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
In the same journal
Applied Soft Computing
Other Engineering and Technologies not elsewhere specified

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 41 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf