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Modified Choice Function Heuristic Selection for the Multidimensional Knapsack Problem

  • John H. DrakeEmail author
  • Ender Özcan
  • Edmund K. Burke
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 329)

Abstract

Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using Modified Choice Function heuristic selection can be effective at solving problems in multiple problem domains. Late Acceptance Strategy is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a Modified Choice Function - Late Acceptance Strategy hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks.

Keywords

Hyper-heuristics Choice Function Heuristic Selection Multidimensional Knapsack Problem Combinatorial Optimization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • John H. Drake
    • 1
    Email author
  • Ender Özcan
    • 1
  • Edmund K. Burke
    • 2
  1. 1.ASAP Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.Computing Science and Mathematics, School of Natural SciencesUniversity of StirlingStirlingScotland, UK

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