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The Scientific World Journal
Volume 2014, Article ID 506769, 9 pages
http://dx.doi.org/10.1155/2014/506769
Research Article

An Encoding Technique for Multiobjective Evolutionary Algorithms Applied to Power Distribution System Reconfiguration

1Instituto Tecnológico de Morelia, Avenida Tecnológico 1500, 58120 Morelia, MICH, Mexico
2Instituto Tecnológico Superior de Irapuato, Carretera Irapuato, Silao Km. 12.5, 36821 Irapuato, GTO, Mexico

Received 7 May 2014; Accepted 3 September 2014; Published 23 October 2014

Academic Editor: Manoj Jha

Copyright © 2014 J. L. Guardado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • M.A. Leon, J.L. Guardado, F. Rivas-Davalos, and E. Melgoza, “A comparative study of multi-objective optimization methods for power distribution system reconfiguration,” 2016 IEEE PES Transmission & Distribution Conference and Exposition-Latin America (PES T&D-LA), pp. 1–6, . View at Publisher · View at Google Scholar
  • Edwin Garcia, Roberto Hincapie, Esteban Inga, Diego Carrion, Alexander Aguila, and González, “Minimal Deployment and Routing Geographic of PMUs on Electrical Power System based on MST Algorithm,” IEEE Latin America Transactions, vol. 14, no. 5, pp. 2264–2270, 2016. View at Publisher · View at Google Scholar
  • Daniel Xavier Sousa, Sérgio Canuto, Marcos André Gonçalves, Thierson Couto Rosa, and Wellington Santos Martins, “Risk-Sensitive Learning to Rank with Evolutionary Multi-Objective Feature Selection,” ACM Transactions on Information Systems, vol. 37, no. 2, pp. 1–34, 2019. View at Publisher · View at Google Scholar