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

  • Juan Carlos Gomez
  • Hugo Terashima-Marín
Part of the following topical collections:
  1. Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation


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.


Bin packing problem Evolutionary computation Hyper-heuristics Heuristics Multi-objective optimization Genetic algorithms 



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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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electronics, División de IngenieríasUniversidad de Guanajuato Campus Irapuato-SalamancaSalamancaMexico
  2. 2.School of Engineering and SciencesTecnológico de MonterreyMonterreyMexico

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