A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance

Adriana Menchaca-Mendez, Elizabeth Montero, Luis Miguel Antonio, Saul Zapotecas-Martinez, Carlos A.Coello Coello Coello, Maria Cristina Riff

Resultado de la investigación: Article

Resumen

Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the ϵ-dominance has shown a proper balance between convergence and diversity. When using ϵ-dominance, diversity is ensured by partitioning the objective space into boxes of size ϵ and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of ϵ. In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of ϵ along the search without any need of a previous user's knowledge. We include the proposed co-evolutionary scheme into an MOEA based on ϵ-dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning state-of-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient.

Idioma originalEnglish
Número de artículo8637162
Páginas (desde-hasta)18267-18283
Número de páginas17
PublicaciónIEEE Access
Volumen7
DOI
EstadoPublished - 1 ene 2019

Huella dactilar

Evolutionary algorithms
Multiobjective optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Citar esto

Menchaca-Mendez, A., Montero, E., Antonio, L. M., Zapotecas-Martinez, S., Coello Coello, C. A. C., & Riff, M. C. (2019). A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance. IEEE Access, 7, 18267-18283. [8637162]. https://doi.org/10.1109/ACCESS.2019.2896962
Menchaca-Mendez, Adriana ; Montero, Elizabeth ; Antonio, Luis Miguel ; Zapotecas-Martinez, Saul ; Coello Coello, Carlos A.Coello ; Riff, Maria Cristina. / A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance. En: IEEE Access. 2019 ; Vol. 7. pp. 18267-18283.
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Menchaca-Mendez, A, Montero, E, Antonio, LM, Zapotecas-Martinez, S, Coello Coello, CAC & Riff, MC 2019, 'A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance', IEEE Access, vol. 7, 8637162, pp. 18267-18283. https://doi.org/10.1109/ACCESS.2019.2896962

A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance. / Menchaca-Mendez, Adriana; Montero, Elizabeth; Antonio, Luis Miguel; Zapotecas-Martinez, Saul; Coello Coello, Carlos A.Coello; Riff, Maria Cristina.

En: IEEE Access, Vol. 7, 8637162, 01.01.2019, p. 18267-18283.

Resultado de la investigación: Article

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AU - Montero, Elizabeth

AU - Antonio, Luis Miguel

AU - Zapotecas-Martinez, Saul

AU - Coello Coello, Carlos A.Coello

AU - Riff, Maria Cristina

PY - 2019/1/1

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N2 - Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the ϵ-dominance has shown a proper balance between convergence and diversity. When using ϵ-dominance, diversity is ensured by partitioning the objective space into boxes of size ϵ and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of ϵ. In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of ϵ along the search without any need of a previous user's knowledge. We include the proposed co-evolutionary scheme into an MOEA based on ϵ-dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning state-of-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient.

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Menchaca-Mendez A, Montero E, Antonio LM, Zapotecas-Martinez S, Coello Coello CAC, Riff MC. A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance. IEEE Access. 2019 ene 1;7:18267-18283. 8637162. https://doi.org/10.1109/ACCESS.2019.2896962