  We address a challenge of active flow control: the optimization of many
actuation parameters guaranteeing fast convergence and avoiding suboptimal
local minima. This challenge is addressed by a new optimizer, called
explorative gradient method (EGM). EGM alternatively performs one exploitive
downhill simplex step and an explorative Latin hypercube sampling iteration.
Thus, the convergence rate of a gradient based method is guaranteed while, at
the same time, better minima are explored. For an analytical multi-modal test
function, EGM is shown to significantly outperform the downhill simplex method,
the random restart variant, Latin hypercube sampling, Monte Carlo iterations
and the genetic algorithm. EGM is applied to minimize the net drag power of the
two-dimensional fluidic pinball benchmark with three cylinder rotations as
actuation parameters. The net drag power is reduced by $40\%$ employing direct
numerical simulations at a Reynolds number of $100$ based on the cylinder
diameter. This optimal actuation leads to $98\%$ drag reduction employing
Coanda forcing for boat tailing and partial stabilization of vortex shedding.
The price is an actuation energy corresponding to $58\%$ of the unforced
parasitic drag power. EGM is also used to minimize drag of the $35^\circ$
slanted Ahmed body employing distributed steady blowing with 10 inputs. $17\%$
drag reduction are achieved using Reynolds-Averaged Navier-Stokes simulations
(RANS) at the Reynolds number $Re_H=1.9 \times 10^5$ based on the height of the
Ahmed body. The optimal actuation emulates boat tailing by inward-directed
blowing with velocities which are comparable to the oncoming velocity. We
expect that EGM will be employed as efficient optimizer in many future active
flow control plants.
