Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide con...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 67; S. 100960
Hauptverfasser: Falcón-Cardona, Jesús Guillermo, Hernández Gómez, Raquel, Coello Coello, Carlos A., Castillo Tapia, Ma. Guadalupe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2021
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ISSN:2210-6502
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Abstract Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed.
AbstractList Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed.
ArticleNumber 100960
Author Falcón-Cardona, Jesús Guillermo
Hernández Gómez, Raquel
Coello Coello, Carlos A.
Castillo Tapia, Ma. Guadalupe
Author_xml – sequence: 1
  givenname: Jesús Guillermo
  orcidid: 0000-0003-1131-098X
  surname: Falcón-Cardona
  fullname: Falcón-Cardona, Jesús Guillermo
  email: jfalcon@tec.mx
  organization: CINVESTAV-IPN, Department of Computer Science, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico City, Mexico, 07300
– sequence: 2
  givenname: Raquel
  surname: Hernández Gómez
  fullname: Hernández Gómez, Raquel
  email: rhernandez@computacion.cs.cinvestav.mx
  organization: CINVESTAV-IPN, Department of Computer Science, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico City, Mexico, 07300
– sequence: 3
  givenname: Carlos A.
  surname: Coello Coello
  fullname: Coello Coello, Carlos A.
  email: ccoello@cs.cinvestav.mx
  organization: CINVESTAV-IPN, Department of Computer Science, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico City, Mexico, 07300
– sequence: 4
  givenname: Ma. Guadalupe
  surname: Castillo Tapia
  fullname: Castillo Tapia, Ma. Guadalupe
  email: mgct@azc.uam.mx
  organization: UAM Azcapotzalco, Departamento de Administración, Av. San Pablo No. 180, Mexico City, Mexico, 02200
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Parallel computing
Multi-objective optimization
Evolutionary algorithms
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Snippet Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of...
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StartPage 100960
SubjectTerms Evolutionary algorithms
Multi-objective optimization
Parallel computing
Title Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey
URI https://dx.doi.org/10.1016/j.swevo.2021.100960
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