An Analysis of Parameters of Decomposition-Based MOEAs on Many-Objective Optimization

Elizabeth Montero, Saúl Zapotecas-Martínez

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

This paper presents an analysis of parameters defined within some of the most representative decomposition-based multi-objective evolutionary algorithms (MOEAs) namely: MOEA/D, MOEA/D-DE, and MOEA/D-DRA. Our main interest is focused in the many-objective context, where decomposition-based MOEAs have been successfully applied, but a lack of analysis on the relevance of their parameters is evidently observable. We review the literature related to those parameter values that have been commonly adopted and we perform some experiments oriented to validate these decisions. Our results show that some alternative parameter configurations can allow these methods to obtain better solutions than the standard values. Moreover, some of our recommendations can conduct to inspect in detail the design of these algorithms.

Idioma originalInglés
Título de la publicación alojada2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781509060177
DOI
EstadoPublicada - 28 sep 2018
Evento2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brasil
Duración: 8 jul 201813 jul 2018

Conferencia

Conferencia2018 IEEE Congress on Evolutionary Computation, CEC 2018
PaísBrasil
CiudadRio de Janeiro
Período8/07/1813/07/18

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Control y optimización

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  • Citar esto

    Montero, E., & Zapotecas-Martínez, S. (2018). An Analysis of Parameters of Decomposition-Based MOEAs on Many-Objective Optimization. En 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings [8477648] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477648