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Global optimization based on local searches

Author

Listed:
  • Marco Locatelli

    (Università di Parma)

  • Fabio Schoen

    (Università di Firenze)

Abstract

In this paper we deal with the use of local searches within global optimization algorithms. We discuss different issues, such as the generation of new starting points, the strategies to decide whether to start a local search from a given point, and those to decide whether to keep the point or discard it from further consideration. We present how these topics have been faced in the existing literature and express our opinion on the relative merits of different choices.

Suggested Citation

  • Marco Locatelli & Fabio Schoen, 2016. "Global optimization based on local searches," Annals of Operations Research, Springer, vol. 240(1), pages 251-270, May.
    • Handle: RePEc:spr:annopr:v:240:y:2016:i:1:d:10.1007_s10479-015-2014-2
    DOI: 10.1007/s10479-015-2014-2
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    References listed on IDEAS

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    1. A. Cassioli & D. Di Lorenzo & M. Locatelli & F. Schoen & M. Sciandrone, 2012. "Machine learning for global optimization," Computational Optimization and Applications, Springer, vol. 51(1), pages 279-303, January.
    2. Mladenovic, Nenad & Drazic, Milan & Kovacevic-Vujcic, Vera & Cangalovic, Mirjana, 2008. "General variable neighborhood search for the continuous optimization," European Journal of Operational Research, Elsevier, vol. 191(3), pages 753-770, December.
    3. Y. Petalas & K. Parsopoulos & M. Vrahatis, 2007. "Memetic particle swarm optimization," Annals of Operations Research, Springer, vol. 156(1), pages 99-127, December.
    4. Georgieva, A. & Jordanov, I., 2009. "Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms," European Journal of Operational Research, Elsevier, vol. 196(2), pages 413-422, July.
    5. Jonathan P. K. Doye & Robert H. Leary & Marco Locatelli & Fabio Schoen, 2004. "Global Optimization of Morse Clusters by Potential Energy Transformations," INFORMS Journal on Computing, INFORMS, vol. 16(4), pages 371-379, November.
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    Cited by:

    1. Logan Mathesen & Giulia Pedrielli & Szu Hui Ng & Zelda B. Zabinsky, 2021. "Stochastic optimization with adaptive restart: a framework for integrated local and global learning," Journal of Global Optimization, Springer, vol. 79(1), pages 87-110, January.

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