Abstract
In the paper, we investigate the pair frequency of low-level heuristics for the bin packing problem and propose a Frequency Distribution based Hyper-Heuristic (FDHH). FDHH generates the heuristic sequences based on a pair of low-level heuristics rather than an individual low-level heuristic. An existing Simulated Annealing Hyper-Heuristic (SAHH) is employed to form the pair frequencies and is extended to guide the further selection of low-level heuristics. To represent the frequency distribution, a frequency matrix is built to collect the pair frequencies while a reverse-frequency matrix is generated to avoid getting trapped into the local optima. The experimental results on the bin-packing problems show that FDHH can obtain optimal solutions on more instances than the original hyper-heuristic.
Our work is partially supported by the Natural Science Foundation of China under Grant No. 60805024, 60903049, 61033012, the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20070141020, CAS Innovation Program under Grant No. ISCAS2009-DR01, and Natural Science Foundation of Dalian under Grant NO. 201000117.
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Jiang, H., Zhang, S., Xuan, J., Wu, Y. (2011). Frequency Distribution Based Hyper-Heuristic for the Bin-Packing Problem. In: Merz, P., Hao, JK. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2011. Lecture Notes in Computer Science, vol 6622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20364-0_11
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