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Application of Stochastic Global Optimization Algorithms to Practical Problems

Author

Listed:
  • M. M. Ali

    (Åbo Akademi University, Turku Center for Computer Science)

  • C. Storey

    (DeMontfort University)

  • A. Törn

    (Åbo Akademi University)

Abstract

We describe global optimization problems from three different fields representing many-body potentials in physical chemistry, optimal control of a chemical reactor, and fitting a statistical model to empirical data. Historical background for each of the problems as well as the practical significance of the first two are given. The problems are solved by using eight recently developed stochastic global optimization algorithms representing controlled random search (4 algorithms), simulated annealing (2 algorithms), and clustering (2 algorithms). The results are discussed, and the importance of global optimization in each respective field is focused.

Suggested Citation

  • M. M. Ali & C. Storey & A. Törn, 1997. "Application of Stochastic Global Optimization Algorithms to Practical Problems," Journal of Optimization Theory and Applications, Springer, vol. 95(3), pages 545-563, December.
    • Handle: RePEc:spr:joptap:v:95:y:1997:i:3:d:10.1023_a:1022617804737
    DOI: 10.1023/A:1022617804737
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    Citations

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    Cited by:

    1. Takeshi Tsuchiya, 2012. "Global optimization of polynomial-expressed nonlinear optimal control problems with semidefinite programming relaxation," Journal of Global Optimization, Springer, vol. 54(4), pages 831-854, December.
    2. P. Kaelo & M. M. Ali, 2006. "Some Variants of the Controlled Random Search Algorithm for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 130(2), pages 253-264, August.
    3. Ziwei Lin & Andrea Matta & Sichang Du & Evren Sahin, 2022. "A Partition-Based Random Search Method for Multimodal Optimization," Mathematics, MDPI, vol. 11(1), pages 1-30, December.
    4. Ali, M.M., 2007. "Synthesis of the [beta]-distribution as an aid to stochastic global optimization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 133-149, September.

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