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A Deterministic Global Optimization Algorithm for Design Problems

In: Essays and Surveys in Global Optimization

Author

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  • Frédéric Messine

Abstract

Complete extensions of standard deterministic Branch-and-Bound algorithms based on interval analysis are presented hereafter in order to solve design problems which can be formulated as non-homogeneous mixed-constrained global optimization problems. This involves the consideration of variables of different kinds: real, integer, logical or categorical. In order to solve interesting design problems with an important number of variables, some accelerating procedures must be introduced in these extended algorithms. They are based on constraint propagation techniques and are explained in this chapter. In order to validate the designing methodology, rotating machines with permanent magnets are considered. The corresponding analytical model is recalled and some global optimal design solutions are presented and discussed.

Suggested Citation

  • Frédéric Messine, 2005. "A Deterministic Global Optimization Algorithm for Design Problems," Springer Books, in: Charles Audet & Pierre Hansen & Gilles Savard (ed.), Essays and Surveys in Global Optimization, chapter 0, pages 267-294, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-25570-5_10
    DOI: 10.1007/0-387-25570-2_10
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    Cited by:

    1. Jordan Ninin & Frédéric Messine, 2011. "A metaheuristic methodology based on the limitation of the memory of interval branch and bound algorithms," Journal of Global Optimization, Springer, vol. 50(4), pages 629-644, August.
    2. Ignacio Araya & Victor Reyes, 2016. "Interval Branch-and-Bound algorithms for optimization and constraint satisfaction: a survey and prospects," Journal of Global Optimization, Springer, vol. 65(4), pages 837-866, August.
    3. Emilio Carrizosa & Frédéric Messine, 2021. "An interval branch and bound method for global Robust optimization," Journal of Global Optimization, Springer, vol. 80(3), pages 507-522, July.

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