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A general-purpose global optimizer: Implimentation and applications

Author

Listed:
  • Pronzato, Luc
  • Walter, Eric
  • Venot, Alain
  • Lebruchec, Jean-Francois

Abstract

This paper, written from a user stand-point, advocates the Adaptive Random Search strategy as an efficient tool for global optimization. First is presented a brief overview of the various types of methods available in the literature for global optimization, and practical advantages of the random search approach are advanced. Some modifications, which were found to improve the efficiency and versatility of the method, and a detailed description of the practical implementation of the resulting algorithm are presented. The routine is used first to treat seven test-cases from the literature for comparison purposes. Then two examples are treated related to automatic control theory. The first one is a parameter estimation problem. the second one a control problem. Finally a practical application of the method to automated registration in medical nuclear imagery is presented.

Suggested Citation

  • Pronzato, Luc & Walter, Eric & Venot, Alain & Lebruchec, Jean-Francois, 1984. "A general-purpose global optimizer: Implimentation and applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 26(5), pages 412-422.
    • Handle: RePEc:eee:matcom:v:26:y:1984:i:5:p:412-422
    DOI: 10.1016/0378-4754(84)90105-8
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    References listed on IDEAS

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    1. Bekey, George A. & Masri, Sami F., 1983. "Random search techniques for optimization of nonlinear systems with many parameters," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 25(3), pages 210-213.
    2. Rubinstein, Y.R. & Samorodnitsky, G., 1982. "Efficiency of the random search method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 24(4), pages 257-268.
    3. Francisco J. Solis & Roger J.-B. Wets, 1981. "Minimization by Random Search Techniques," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 19-30, February.
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    Citations

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    1. Walter, E. & Piet-Lahanier, H. & Happel, J., 1986. "Estimation of non-uniquely identifiable parameters via exhaustive modeling and membership set theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(6), pages 479-490.
    2. Kumar, Rajeeva & Kabamba, Pierre T. & Hyland, David C., 2005. "Analysis and parameter selection for an adaptive random search algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 95-103.
    3. Piet-Lahanier, H. & Walter, E., 1990. "Characterization of non-connected parameter uncertainty regions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(5), pages 553-560.
    4. Walter, Eric & Piet-Lahanier, Hélène, 1990. "Estimation of parameter bounds from bounded-error data: a survey," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(5), pages 449-468.
    5. Pronzato, Luc & Walter, Eric, 1990. "Experiment design for bounded-error models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(5), pages 571-584.

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