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

Elizabeth Montero, Saúl Zapotecas-Martínez

Resultado de la investigación: Conference contribution

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 originalEnglish
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
EstadoPublished - 28 sep 2018
Evento2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duración: 8 jul 201813 jul 2018

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
PaísBrazil
CiudadRio de Janeiro
Período8/07/1813/07/18

Huella dactilar

Multi-objective Evolutionary Algorithm
Evolutionary algorithms
Decomposition
Decompose
Optimization
Design of Algorithms
Recommendations
Configuration
Alternatives
Experiment
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Optimization

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
Montero, Elizabeth ; Zapotecas-Martínez, Saúl. / An Analysis of Parameters of Decomposition-Based MOEAs on Many-Objective Optimization. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018.
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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., 2018 IEEE Congress on Evolutionary Computation, CEC 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/CEC.2018.8477648

An Analysis of Parameters of Decomposition-Based MOEAs on Many-Objective Optimization. / Montero, Elizabeth; Zapotecas-Martínez, Saúl.

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477648.

Resultado de la investigación: Conference contribution

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AB - 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.

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