Stochastic global optimization methods are methods for solving a global optimization problem incorporating probabilistic (stochastic) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself, or in both.
Global optimization is a very important part of applied mathematics and computer science. The importance of global optimization is primarily related to the applied areas such as engineering, computational chemistry, finance and medicine amongst many other fields. For the state of the art in the theory and methodology of global optimization we refer to the “Journal of Global Optimization” and two volumes of the “Handbook of Global Optimization” (Horst and Pardalos 1995; Pardalos and Romeijn 2002). If the objective function is given as a “black box” computer code, the optimization problem is especially difficult. Stochastic approaches can often deal with problems of this kind much easier and more efficiently than the deterministic...
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References and Further Reading
Glover F, Kochenberger GA (2003) Handbook on metaheuristics. Kluwer Academic, Dordrecht
Horst R, Pardalos P (eds) (1995) Handbook of global optimization. Kluwer Academic, Dordrecht
Pardalos P, Romeijn E (eds) (2002) Handbook of global optimization, vol 2. Kluwer Academic, Dordrecht
Van Laarhoven PJM, Aarts EHL (1987) Simulated annealing: theory and applications. D. Reidel, Dordrecht
Zhigljavsky A, Zilinskas A (2008) Stochastic global optimization. Springer, New York
Zhigljavsky A (1991) Theory of global random search. Kluwer Academic, Dordrecht
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Zhigljavsky, A. (2011). Stochastic Global Optimization. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_570
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