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Handbook of Metaheuristics pp 355–368Cite as

Multi-Start Methods

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 57))

Abstract

Heuristic search procedures that aspire to find global 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 linear ordering problem in terms of solution quality and diversification power.

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Author information

Authors and Affiliations

  1. Dpto de Estadística e Investigación Operativa, Universitat de València, Dr. Moliner 50, 46100, Burjassot, Valencia, Spain

    Rafael Martí

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  1. Rafael Martí
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Editor information

Editors and Affiliations

  1. Leeds School of Business, University of Colorado at Boulder, USA

    Fred Glover

  2. College of Business, University of Colorado at Denver, USA

    Gary A. Kochenberger

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© 2003 Kluwer Academic Publishers

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Martí, R. (2003). Multi-Start Methods. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 57. Springer, Boston, MA. https://doi.org/10.1007/0-306-48056-5_12

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  • DOI: https://doi.org/10.1007/0-306-48056-5_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7263-5

  • Online ISBN: 978-0-306-48056-0

  • eBook Packages: Springer Book Archive

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