  Control of chaotic systems to given targets is a subject of substantial and
well-developed research issue in nonlinear science, which can be formulated as
a class of multi-modal constrained numerical optimization problem with
multi-dimensional decision variables. This investigation elucidates the
feasibility of applying a novel population-based metaheuristics labelled here
as Teaching-learning-based optimization to direct the orbits of discrete
chaotic dynamical systems towards the desired target region. Several
consecutive control steps of small bounded perturbations are made in the
Teaching-learning-based optimization strategy to direct the chaotic series
towards the optimal neighborhood of the desired target rapidly, where a
conventional controller is effective for chaos control. Working with the
dynamics of the well-known Henon as well as Ushio discrete chaotic systems, we
assess the effectiveness and efficiency of the Teaching-learning-based
optimization based optimal control technique, meanwhile the impacts of the core
parameters on performances are also discussed. Furthermore, possible
engineering applications of directing chaotic orbits are discussed.
