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Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems

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Abstract

In this article, a multi-objective evolutionary framework to build selection hyper-heuristics for solving instances of the 2D bin packing problem is presented. The approach consists of a multi-objective evolutionary learning process, using specific tailored genetic operators, to produce sets of variable length rules representing hyper-heuristics. Each hyper-heuristic builds a solution to a given problem instance by sensing the state of the instance, and deciding which single heuristic to apply at each decision point. The hyper-heuristics consider the minimization of two conflicting objectives when building a solution: the number of bins used to accommodate the pieces and the total time required to do the job. The proposed framework integrates three well-studied multi-objective evolutionary algorithms to produce sets of Pareto-approximated hyper-heuristics: the Non-dominated Sorting Genetic Algorithm-II, the Strength Pareto Evolutionary Algorithm 2, and the Generalized Differential Evolution Algorithm 3. We conduct an extensive experimental analysis using a large set of 2D bin packing problem instances containing convex and non-convex irregular pieces, under many conditions, settings and using several performance metrics. The analysis assesses the robustness and flexibility of the proposed approach, providing encouraging results when compared against a set of well-known baseline single heuristics.

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Notes

  1. The set of problem instances used in this work can be found at http://paginas.fe.up.pt/~esicup/datasets Terashima1-Terashima2

  2. Available at: http://moeaframework.org/index.html.

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Acknowledgements

This research was supported in part by CONACyT Basic Science Projects under Grants 99695 and 241461, ITESM Research Group with Strategic Focus in intelligent Systems, and by Universidad de Guanajuato Campus Irapuato-Salamanca.

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Authors and Affiliations

  1. Department of Electronics, División de Ingenierías, Universidad de Guanajuato Campus Irapuato-Salamanca, Carretera Salamanca - Valle de Santiago km 3.5 + 1.8, Comunidad de Palo Blanco, Salamanca, Guanajuato, Mexico

    Juan Carlos Gomez

  2. School of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Colonia Tecnológico, Monterrey, 64849, Nuevo Leon, Mexico

    Hugo Terashima-Marín

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  1. Juan Carlos Gomez
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Correspondence to Juan Carlos Gomez.

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Gomez, J.C., Terashima-Marín, H. Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems. Genet Program Evolvable Mach 19, 151–181 (2018). https://doi.org/10.1007/s10710-017-9301-4

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