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A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

Author

Listed:
  • H. Faria

    (UFABC – Universidade Federal do ABC
    Université de Liège)

  • M. G. C. Resende

    (Mathematical Optimization and Planning, Amazon.com)

  • D. Ernst

    (Université de Liège)

Abstract

This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations.

Suggested Citation

  • H. Faria & M. G. C. Resende & D. Ernst, 2017. "A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem," Journal of Heuristics, Springer, vol. 23(6), pages 533-550, December.
    • Handle: RePEc:spr:joheur:v:23:y:2017:i:6:d:10.1007_s10732-017-9355-8
    DOI: 10.1007/s10732-017-9355-8
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    References listed on IDEAS

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    1. L. A. C. Roque & D. B. M. M. Fontes & F. A. C. C. Fontes, 2014. "A hybrid biased random key genetic algorithm approach for the unit commitment problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 140-166, July.
    2. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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    Cited by:

    1. Karla B. Freitas & Márcio S. Arantes & Claudio F. M. Toledo & Alexandre C. B. Delbem, 2020. "MIQP model and improvement heuristic for power loss minimization in distribution system with network reconfiguration," Journal of Heuristics, Springer, vol. 26(1), pages 59-81, February.
    2. Cheng-Jian Lin & Chun-Hui Lin, 2021. "Using an Improved Differential Evolution for Scheduling Optimization of Dual-Gantry Multi-Head Surface-Mount Placement Machine," Mathematics, MDPI, vol. 9(16), pages 1-22, August.

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