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
Schlagworte:	Evolutionary algorithms Multi-objective optimization Parallel computing 99-00 Parallel computing Multi-objective optimization Evolutionary algorithms 00-01
ISSN:	2210-6502
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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.
ISSN:	2210-6502
DOI:	10.1016/j.swevo.2021.100960