A survey and classification of Opposition-Based Metaheuristics

Nicolás Rojas-Morales, María Cristina Riff Rojas, Elizabeth Montero Ureta

Resultado de la investigación: Article

7 Citas (Scopus)

Resumen

Opposition-Based Learning (OBL) is a research area that has been widely applied in several algorithms for improving the search process. In this work we present a revision of several applications of OBL in metaheuristics and some metaheuristic approaches that are inspired in OBL. For reviewing each OBL approach we analyze the objective of including OBL, the role performed by the OBL component, the type of OBL and the type of problem tackled. We also propose a classification of these approaches that apply or are inspired in OBL. Our goal is to motivate researchers in metaheuristics to include ideas from OBL and report which strategies were successfully applied.

Idioma originalEnglish
Páginas (desde-hasta)424-435
Número de páginas12
PublicaciónComputers and Industrial Engineering
Volumen110
DOI
EstadoPublished - 1 ago 2017

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Citar esto

Rojas-Morales, Nicolás ; Riff Rojas, María Cristina ; Montero Ureta, Elizabeth. / A survey and classification of Opposition-Based Metaheuristics. En: Computers and Industrial Engineering. 2017 ; Vol. 110. pp. 424-435.
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A survey and classification of Opposition-Based Metaheuristics. / Rojas-Morales, Nicolás; Riff Rojas, María Cristina; Montero Ureta, Elizabeth.

En: Computers and Industrial Engineering, Vol. 110, 01.08.2017, p. 424-435.

Resultado de la investigación: Article

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