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Simulated Annealing and Artificial Bee Colony for the Redistricting Process in Mexico

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  • Miguel Ángel Gutiérrez-Andrade

    (Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana Unidad Iztapalapa, 09340 Mexico City, Mexico)

  • Eric Alfredo Rincón-García

    (Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana Unidad Iztapalapa, 09340 Mexico City, Mexico)

  • Sergio Gerardo de-los-Cobos-Silva

    (Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana Unidad Iztapalapa, 09340 Mexico City, Mexico)

  • Pedro Lara-Velázquez

    (Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana Unidad Iztapalapa, 09340 Mexico City, Mexico)

  • Roman Anselmo Mora-Gutiérrez

    (Departamento de Sistemas, Universidad Autónoma Metropolitana Unidad Azcapotzalco, 02200 Mexico City, Mexico)

  • Antonin Ponsich

    (Departamento de Sistemas, Universidad Autónoma Metropolitana Unidad Azcapotzalco, 02200 Mexico City, Mexico)

Abstract

Redistricting is the redrawing of the boundaries of legislative districts for electoral purposes in such a way that these boundaries fulfill federal and/or state requirements. From 2015 to 2017, the National Electoral Institute of Mexico carried out the redistricting process of all 32 Mexican federal entities using a nonlinear programming model in which population equality and compactness were considered as conflicting objective functions, but other criteria, such as contiguity, travel times between municipalities, and indigenous population, were included as hard constraints. To find high-quality redistricting plans within acceptable time limits, we designed two optimization algorithms; one is based on simulated annealing and the other on artificial bee colony. In this paper, we describe our methodology and the results we obtained when we used these algorithms for this redistricting process.

Suggested Citation

  • Miguel Ángel Gutiérrez-Andrade & Eric Alfredo Rincón-García & Sergio Gerardo de-los-Cobos-Silva & Pedro Lara-Velázquez & Roman Anselmo Mora-Gutiérrez & Antonin Ponsich, 2019. "Simulated Annealing and Artificial Bee Colony for the Redistricting Process in Mexico," Interfaces, INFORMS, vol. 49(3), pages 189-200, May.
    • Handle: RePEc:inm:orinte:v:49:y:2019:i:3:p:189-200
    DOI: 10.1287/inte.2019.0992
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    References listed on IDEAS

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    1. W. Macmillan, 2001. "Redistricting in a GIS environment: An optimisation algorithm using switching-points," Journal of Geographical Systems, Springer, vol. 3(2), pages 167-180, August.
    2. Ricca, Federica & Simeone, Bruno, 2008. "Local search algorithms for political districting," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1409-1426, September.
    3. Kenneth C. Gilbert & David D. Holmes & Richard E. Rosenthal, 1985. "A Multiobjective Discrete Optimization Model for Land Allocation," Management Science, INFORMS, vol. 31(12), pages 1509-1522, December.
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