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Multi-wave algorithms for metaheuristic optimization

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  • Fred Glover

    (University of Colorado)

Abstract

We propose new iterated improvement neighborhood search algorithms for metaheuristic optimization by exploiting notions of conditional influence within a strategic oscillation framework. These approaches, which are unified within a class of methods called multi-wave algorithms, offer further refinements by memory based strategies that draw on the concept of persistent attractiveness. Our algorithms provide new forms of both neighborhood search methods and multi-start methods, and are readily embodied within evolutionary algorithms and memetic algorithms by solution combination mechanisms derived from path relinking. These methods can also be used to enhance branching strategies for mixed integer programming.

Suggested Citation

  • Fred Glover, 2016. "Multi-wave algorithms for metaheuristic optimization," Journal of Heuristics, Springer, vol. 22(3), pages 331-358, June.
    • Handle: RePEc:spr:joheur:v:22:y:2016:i:3:d:10.1007_s10732-016-9312-y
    DOI: 10.1007/s10732-016-9312-y
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    References listed on IDEAS

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    1. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    2. Fred Glover, 1995. "Tabu Thresholding: Improved Search by Nonmonotonic Trajectories," INFORMS Journal on Computing, INFORMS, vol. 7(4), pages 426-442, November.
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    Cited by:

    1. Jiawei Song & Yang Wang & Haibo Wang & Qinghua Wu & Abraham P. Punnen, 2019. "An effective multi-wave algorithm for solving the max-mean dispersion problem," Journal of Heuristics, Springer, vol. 25(4), pages 731-752, October.
    2. Alice E. Smith, 2019. "Note from the Editor," INFORMS Journal on Computing, INFORMS, vol. 31(3), pages 411-412, July.

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