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Intelligent Multi-Start Methods

In: Handbook of Metaheuristics

Author

Listed:
  • Rafael Martí

    (Universidad de Valencia)

  • Ricardo Aceves

    (Universidad Nacional Autónoma de México)

  • Maria Teresa León

    (Universidad de La Laguna)

  • Jose M. Moreno-Vega

    (Universidad Rey Juan Carlos)

  • Abraham Duarte

    (Universidad de Valencia)

Abstract

Heuristic search procedures aimed at finding globally optimal solutions to hard combinatorial optimization problems usually require some type of diversification to overcome local optimality. One way to achieve diversification is to re-start the procedure from a new solution once a region has been explored, which constitutes a multi-start procedure. In this chapter we describe the best known multi-start methods for solving optimization problems. We also describe their connections with other metaheuristic methodologies. We propose classifying these methods in terms of their use of randomization, memory and degree of rebuild. We also present a computational comparison of these methods on solving the Maximum Diversity Problem to illustrate the efficiency of the multi-start methodology in terms of solution quality and diversification power.

Suggested Citation

  • Rafael Martí & Ricardo Aceves & Maria Teresa León & Jose M. Moreno-Vega & Abraham Duarte, 2019. "Intelligent Multi-Start Methods," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 221-243, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-91086-4_7
    DOI: 10.1007/978-3-319-91086-4_7
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    Cited by:

    1. Wang, Haibo & Alidaee, Bahram, 2019. "The multi-floor cross-dock door assignment problem: Rising challenges for the new trend in logistics industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 30-47.

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