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Advanced Multi-start Methods

In: Handbook of Metaheuristics

Author

Listed:
  • R. Martí

    (Universidad de Valencia)

  • J. Marcos Moreno-Vega

    (Universidad de La Laguna, La Laguna Santa Cruz de Tenerife)

  • A. Duarte

    (Universidad Rey Juan Carlos)

Abstract

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.

Suggested Citation

  • R. Martí & J. Marcos Moreno-Vega & A. Duarte, 2010. "Advanced Multi-start Methods," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 265-281, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_9
    DOI: 10.1007/978-1-4419-1665-5_9
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    Citations

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    Cited by:

    1. Alan Lee & Martin Savelsbergh, 2017. "An extended demand responsive connector," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 25-50, March.
    2. Emile Glorieux & Bo Svensson & Fredrik Danielsson & Bengt Lennartson, 2017. "Constructive cooperative coevolution for large-scale global optimisation," Journal of Heuristics, Springer, vol. 23(6), pages 449-469, December.
    3. López-Sánchez, A.D. & Hernández-Díaz, A.G. & Vigo, D. & Caballero, R. & Molina, J., 2014. "A multi-start algorithm for a balanced real-world Open Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 238(1), pages 104-113.
    4. Jian Wang & Muqing Du & Lili Lu & Xiaozheng He, 2018. "Maximizing Network Throughput under Stochastic User Equilibrium with Elastic Demand," Networks and Spatial Economics, Springer, vol. 18(1), pages 115-143, March.
    5. Bruno Nogueira & Rian G. S. Pinheiro, 2020. "A GPU based local search algorithm for the unweighted and weighted maximum s-plex problems," Annals of Operations Research, Springer, vol. 284(1), pages 367-400, January.
    6. Simona Mancini, 2013. "Multi-echelon distribution systems in city logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 54, pages 1-2.
    7. Karapetyan, Daniel & Mitrovic Minic, Snezana & Malladi, Krishna T. & Punnen, Abraham P., 2015. "Satellite downlink scheduling problem: A case study," Omega, Elsevier, vol. 53(C), pages 115-123.
    8. Yue Sun & Alfredo Garcia, 2017. "Interactive model-based search with reactive resource allocation," Journal of Global Optimization, Springer, vol. 67(1), pages 135-149, January.
    9. Weiqi Li, 2011. "Seeking global edges for traveling salesman problem in multi-start search," Journal of Global Optimization, Springer, vol. 51(3), pages 515-540, November.

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