Landscape-scaled strategies can outperform Lévy random searches

Information on the relevant global scales of search space, even if partial, should conceivably enhance performance random searches. Here we show numerically and analytically that paradigmatic uninformed optimal L\'evy searches can be outperformed by informed multiple-scale in one (1D) two (2D) dimensions, when knowledge about landscape is incomplete. We low-density nondestructive regime efficiency biexponential incorporate all key 1D size $L$ decays asymptotically as ${\ensuremath{\eta}}_{\mathrm{opt}}\ensuremath{\sim}1/\sqrt{L}$, overcoming result ${\ensuremath{\eta}}_{\mathrm{opt}}\ensuremath{\sim}1/(\sqrt{L}lnL)$ further characterize level limited information searcher have these scales. obtain phase diagram bi- triexponential 2D. Remarkably, for a certain degree lack information, partially still outperform discuss our results connection with foraging problem.