Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization
Ivan Savin and
Dmitri Blueschke ()
No 2013-051, Jena Economic Research Papers from Friedrich-Schiller-University Jena
Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this baseline framework to nonlinear stochastic problems. However, those algorithms are limited in a variety of ways including, most importantly, restriction to local best solutions only and the symmetry of objective function. In Blueschke et al. (2013a) a new flexible optimization method based on Differential Evolution is suggested. It allows to lift these limitations and achieve better approximations of the policy targets, but is designed to deterministic problems only. This study extends the methodology by dealing with stochastic problems in two different ways: applying extreme event analysis and by minimizing the median objective value. Thus, this research is aimed to broaden the range of decision support information used by policy makers in choosing optimal strategy under much more realistic conditions.
Keywords: Differential evolution; stochstic problems; nonlinear optimization; optimal control (search for similar items in EconPapers)
JEL-codes: C54 C61 E27 E61 E63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-mac and nep-ore
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