Advanced Multi-start Methods

  • R. Martí
  • J. Marcos Moreno-Vega
  • A. Duarte
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)


Heuristic search procedures that aspire to find 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. In this chapter we describe the best known multi-start methods for solving optimization problems. 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 in terms of solution quality and diversification power.



The authors want to thank Prof. Fred Glover for his valuable comments to improve this chapter in both presentation and content. This research was partially supported by the Ministerio de Educación y Ciencia (TIN2006-02696, TIN2009-07516, TIN2009-13363), by the Comunidad de Madrid-Universidad Rey Juan Carlos project (CCG08-URJC/TIC-3731), and by the Gobierno de Canarias project (PI2007/019).


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Departamento de Estadística e Investigación OperativaUniversidad de ValenciaValenciaSpain
  2. 2.Departamento de Estadística, IO y ComputaciónUniversidad de La Laguna, La Laguna Santa Cruz de TenerifeTenerifeSpain
  3. 3.Departamento de Ciencias de la ComputaciónUniversidad Rey Juan CarlosMadridSpain

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