Solving Nonlinear Optimal Control Problems Using Evolutionary Heuristic Optimization

Dmitri Blueschke^*, Viktoria Blüschke-Nikolaeva and Ivan Savin

Policy makers in any country constantly face optimal control problems: what controls one should apply 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. We propose a new flexible optimization method based on Differential Evolution approach. It allows to lift these limitations and to achieve better approximations of the policy targets. Thus, this research is aimed to broaden the range of decision support information used by policy makers in choosing the optimal strategy under much more realistic conditions.

Mathematics Subject Classification: 90X08 37N40

Keywords: stochastic problems; nonlinear optimization; optimal control; differential evolution

Minisymposion: Computational Optimization Methods in Statistics, Econometrics and Finance