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Markov chain modelling of the solution surface in local search

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  • E J Anderson

    (University of New South Wales)

Abstract

When local search methods are applied to combinatorial optimisation problems it is the characteristics of the solution surface that determine the effectiveness of the method. This paper aims to advance our understanding of solution surface characteristics. The focus is on the basin of attraction associated with each local minimum; that is the set of solutions from which a particular local minimum is reached by following downhill local search. A Markov chain model is proposed for the behaviour of the function values occurring in a random walk on the solution surface. The probability transition matrix can be estimated and this is used to estimate both the shape and the size of the basins of attraction. In order to test this approach a study is made of the problem of minimising weighted flowtime on unrelated parallel machines.

Suggested Citation

  • E J Anderson, 2002. "Markov chain modelling of the solution surface in local search," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(6), pages 630-636, June.
  • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:6:d:10.1057_palgrave_jors_2601342
    DOI: 10.1057/palgrave/jors/2601342
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    Cited by:

    1. C-H Lan & C-Y Hsui & L-C Wei, 2005. "A complexity perspective to deploy artistic exhibits," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1151-1158, October.
    2. Mihael Baketarić & Marjan Mernik & Tomaž Kosar, 2021. "Attraction Basins in Metaheuristics: A Systematic Mapping Study," Mathematics, MDPI, vol. 9(23), pages 1-25, November.

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