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Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions

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Abstract

Hyper-heuristics are high level heuristics which coordinate lower level ones to solve a given problem. Low level heuristics, however, are not all as competent/good as each other at solving the given problem and some do not work together as well as others. Hence the idea of measuring how good they are (competence) at solving the problem and how well they work together (their affinity). Models of the affinity and competence properties are suggested and evaluated using previous information on the performance of the simple low level heuristics. The resulting model values are used to improve the performance of the hyper-heuristic by tailoring it not only to the specific problem but the specific instance being solved. The test case is a hard combinatorial problem, namely the Hybrid Flow Shop scheduling problem. Numerical results on randomly generated as well as real-world instances are included.

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Authors and Affiliations

  1. Department of Mathematical Sciences, The University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Abdellah Salhi

  2. Department of Computer Science and Information Technology, The University of Nottingham, Wollaton Road, Nottingham, NG8 1BB, UK

    José Antonio Vázquez Rodríguez

Authors
  1. Abdellah Salhi
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  2. José Antonio Vázquez Rodríguez
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Correspondence to Abdellah Salhi.

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Salhi, A., Vázquez Rodríguez, J.A. Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions. Memetic Comp. 6, 77–84 (2014). https://doi.org/10.1007/s12293-013-0121-7

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  • DOI: https://doi.org/10.1007/s12293-013-0121-7

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