Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Behavior of crossover operators in NSGA-III for large-scale optimization problems
School of Mathematics and Big Data, Foshan University, Foshan, China; School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
School of Mathematics and Big Data, Foshan University, Foshan, China.
Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
Visa övriga samt affilieringar
2020 (Engelska)Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 509, s. 470-487Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

Ort, förlag, år, upplaga, sidor
Elsevier, 2020. Vol. 509, s. 470-487
Nyckelord [en]
Electroencephalography, Large-scale optimization, Big data optimization, Evolutionary multi-objective optimization, NSGA-III, Crossover operator, Performance analysis
Nationell ämneskategori
Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-71519DOI: 10.1016/j.ins.2018.10.005ISI: 000494883700030Scopus ID: 2-s2.0-85055637462OAI: oai:DiVA.org:ltu-71519DiVA, id: diva2:1261941
Anmärkning

Validerad;2019;Nivå 2;2019-10-09 (johcin)

Tillgänglig från: 2018-11-09 Skapad: 2018-11-09 Senast uppdaterad: 2019-11-21Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Vasilakos, Athanasios

Sök vidare i DiVA

Av författaren/redaktören
Vasilakos, Athanasios
Av organisationen
Datavetenskap
I samma tidskrift
Information Sciences
Medieteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 94 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf