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Landscapes, operators and heuristic search

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  • C.R. Reeves

Abstract

Heuristic search methods have been increasingly applied to combinatorial optimizationproblems. While a specific problem defines a unique search space, different “landscapes”are created by the different heuristic search operators used to search it. In this paper, asimple example will be used to illustrate the fact that the landscape structure changes withthe operator; indeed, it often depends even on the way the operators are applied. Recentattention has focused on trying to better understand the nature of these “landscapes”. Recentwork by Boese et al. [2] has shown that instances of the TSP are often characterised by a“big valley” structure in the case of a 2‐opt exchange operator, and a particular distancemetric. In this paper, their work is developed by investigating the question of how landscapeschange under different search operators in the case of the n/m/P/C max flowshop problem.Six operators and four distance metrics are defined, and the resulting landscapes examined.The work is further extended by proposing a statistical randomisation test to provide anumerical assessment of the landscape. Other conclusions relate to the existence of ultra‐metricity,and to the usefulness or otherwise of hybrid neighbourhood operators. Copyright Kluwer Academic Publishers 1999

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  • C.R. Reeves, 1999. "Landscapes, operators and heuristic search," Annals of Operations Research, Springer, vol. 86(0), pages 473-490, January.
  • Handle: RePEc:spr:annopr:v:86:y:1999:i:0:p:473-490:10.1023/a:1018983524911
    DOI: 10.1023/A:1018983524911
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    Cited by:

    1. Nowicki, Eugeniusz & Smutnicki, Czeslaw, 2006. "Some aspects of scatter search in the flow-shop problem," European Journal of Operational Research, Elsevier, vol. 169(2), pages 654-666, March.
    2. Liu, Jiyin & Reeves, Colin R, 2001. "Constructive and composite heuristic solutions to the P//[summation operator]Ci scheduling problem," European Journal of Operational Research, Elsevier, vol. 132(2), pages 439-452, July.
    3. Geiger, Martin Josef, 2007. "On operators and search space topology in multi-objective flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 181(1), pages 195-206, August.
    4. Sašo Karakatič, 2020. "EvoPreprocess—Data Preprocessing Framework with Nature-Inspired Optimization Algorithms," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
    5. Dimitris Fouskakis & David Draper, 2002. "Stochastic Optimization: a Review," International Statistical Review, International Statistical Institute, vol. 70(3), pages 315-349, December.
    6. Ha-Bang Ban, 2021. "A metaheuristic for the delivery man problem with time windows," Journal of Combinatorial Optimization, Springer, vol. 41(4), pages 794-816, May.
    7. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    8. J M Framinan & J N D Gupta & R Leisten, 2004. "A review and classification of heuristics for permutation flow-shop scheduling with makespan objective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1243-1255, December.
    9. Gabriela Ochoa & Nadarajen Veerapen, 2018. "Mapping the global structure of TSP fitness landscapes," Journal of Heuristics, Springer, vol. 24(3), pages 265-294, June.
    10. C R Reeves & A V Eremeev, 2004. "Statistical analysis of local search landscapes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 687-693, July.
    11. Geiger, Martin Josef, 2010. "On heuristic search for the single machine total weighted tardiness problem - Some theoretical insights and their empirical verification," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1235-1243, December.
    12. F J Vasko & D D Newhart & K L Stott & F E Wolf, 2003. "A large-scale application of the partial coverage uncapacitated facility location problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(1), pages 11-20, January.
    13. Zhou, Qing & Benlic, Una & Wu, Qinghua, 2020. "An opposition-based memetic algorithm for the maximum quasi-clique problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 63-83.
    14. Stutzle, Thomas, 2006. "Iterated local search for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1519-1539, November.

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