Stochastic resetting can optimize the intermittent search strategy in a two-dimensional confined topography

Abstract

Based on the intermittent two-state random walk model, by calculating and discussing the mean first capture time, the search efficiency of the intermittent search strategy in a two-dimensional confined topography in the absence and presence of stochastic resetting is studied. Results reveal that for the close target, the regular Brownian motion is the efficient search strategy, whereas, for the target far from the starting point, the typical intermittent search strategy with relocation times being power-law distributed turns out to be the advantageous one, and the search efficiency impressively enhances with the distance of the target from the starting point increasing. We demonstrate that though in the absence of stochastic resetting there does not exist a universal optimal search strategy in the confined topography, the introduction of stochastic resetting makes the Brownian motion surprisingly to be the universal optimal search process, and distinctly outperforms the ones without stochastic resetting.