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Efficient iterated greedy for the two-dimensional bandwidth minimization problem

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  • Cavero, Sergio
  • Pardo, Eduardo G.
  • Duarte, Abraham

Abstract

Graph layout problems are a family of combinatorial optimization problems that consist of finding an embedding of the vertices of an input graph into a host graph such that an objective function is optimized. Within this family of problems falls the so-called Two-Dimensional Bandwidth Minimization Problem (2DBMP). The 2DBMP aims to minimize the maximum distance between each pair of adjacent vertices of the input graph when it is embedded into a grid host graph. In this paper, we present an efficient heuristic algorithm based on the Iterated Greedy (IG) framework hybridized with a new local search strategy to tackle the 2DBMP. Particularly, we propose different designs for the main IG procedures (i.e., construction, destruction, and reconstruction) based on the trade-off between intensification and diversification. Additionally, the improvement method incorporates three advanced strategies: an efficient way to evaluate the objective function of neighbor solutions, a tiebreak criterion to deal with “flat landscapes”, and a neighborhood reduction technique. Extensive experimentation was carried out to assess the IG performance over state-of-the-art methods, emerging our approach as the most competitive algorithm. Specifically, IG finds the best solutions for all instances considered in considerably less execution time. Statistical tests corroborate the merit of our proposal.

Suggested Citation

  • Cavero, Sergio & Pardo, Eduardo G. & Duarte, Abraham, 2023. "Efficient iterated greedy for the two-dimensional bandwidth minimization problem," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1126-1139.
  • Handle: RePEc:eee:ejores:v:306:y:2023:i:3:p:1126-1139
    DOI: 10.1016/j.ejor.2022.09.004
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    References listed on IDEAS

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    1. Mladenovic, Nenad & Urosevic, Dragan & Pérez-Brito, Dionisio & García-González, Carlos G., 2010. "Variable neighbourhood search for bandwidth reduction," European Journal of Operational Research, Elsevier, vol. 200(1), pages 14-27, January.
    2. Sergio Cavero & Eduardo G. Pardo & Abraham Duarte, 2022. "A general variable neighborhood search for the cyclic antibandwidth problem," Computational Optimization and Applications, Springer, vol. 81(2), pages 657-687, March.
    3. Pierre Hansen & Nenad Mladenović & Raca Todosijević & Saïd Hanafi, 2017. "Variable neighborhood search: basics and variants," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 423-454, September.
    4. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    5. Huerta-Muñoz, Diana L. & Ríos-Mercado, Roger Z. & Ruiz, Rubén, 2017. "An iterated greedy heuristic for a market segmentation problem with multiple attributes," European Journal of Operational Research, Elsevier, vol. 261(1), pages 75-87.
    6. Rodriguez-Tello, Eduardo & Hao, Jin-Kao & Torres-Jimenez, Jose, 2008. "An improved simulated annealing algorithm for bandwidth minimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1319-1335, March.
    7. Eduardo G. Pardo & Antonio García-Sánchez & Marc Sevaux & Abraham Duarte, 2020. "Basic variable neighborhood search for the minimum sitting arrangement problem," Journal of Heuristics, Springer, vol. 26(2), pages 249-268, April.
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