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Application of Stochastic Global Optimization Algorithms to Practical Problems

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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.

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Authors and Affiliations

  1. Department of Computer Science, Åbo Akademi University, Turku Center for Computer Science, Turku, Finland

    M. M. Ali (Research Fellow)

  2. Department of Mathematical Sciences, DeMontfort University, Gateway, Leicester, UK

    C. Storey (Research Professor)

  3. Department of Computer Science, Åbo Akademi University, Turku, Finland

    A. Törn (Professor)

Authors
  1. M. M. Ali
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  2. C. Storey
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  3. A. Törn
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Ali, M.M., Storey, C. & Törn, A. Application of Stochastic Global Optimization Algorithms to Practical Problems. Journal of Optimization Theory and Applications 95, 545–563 (1997). https://doi.org/10.1023/A:1022617804737

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