期刊
GENETIC PROGRAMMING AND EVOLVABLE MACHINES
卷 19, 期 1-2, 页码 151-181出版社
SPRINGER
DOI: 10.1007/s10710-017-9301-4
关键词
Bin packing problem; Evolutionary computation; Hyper-heuristics; Heuristics; Multi-objective optimization; Genetic algorithms
资金
- CONACyT [99695, 241461]
- ITESM Research Group with Strategic Focus in intelligent Systems
- Universidad de Guanajuato Campus Irapuato-Salamanca
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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