Optimal Noise in a Stochastic Model for Local Search

TL;DR

This paper presents a stochastic model for local search behavior inspired by fruit fly movement, analyzing how noise influences search efficiency and identifying an optimal noise level for target finding.

Contribution

It introduces a novel two-epoch stochastic model with symmetric alpha-stable noise, capturing the interaction dynamics of searchers with their home and analyzing optimal noise intensity.

Findings

Optimal noise intensity accelerates target discovery.

The model exhibits a noise-dependent relaxation time.

Derived diffusive behavior in the long-term limit.

Abstract

We develop a prototypical stochastic model for local search around a given home. The stochastic dynamic model is motivated by experimental findings of the motion of a fruit fly around a given spot of food but shall generally describe local search behavior. The local search consists of a sequence of two epochs. In the first the searcher explores new space around the home whereas it returns to the home during the second epoch. In the proposed two dimensional model both tasks are described by the same stochastic dynamics. The searcher moves with constant speed and its angular dynamics is driven by a symmetric {\alpha}-stable noise source. The latter stands for the uncertainty to decide the new direction of motion. The main ingredient of the model is the nonlinear interaction dynamics of the searcher with its home. In order to determine the new heading direction, the angles of its position to the home and of the heading vector need to be known. A bound state to the home is realized by a permanent switch of a repulsive and attractive forcing of the heading direction from the position direction corresponding to search and return epochs. Our investigation elucidates the analytic tractability of the deterministic and stochastic dynamics. The noise enables a faster finding of a target distinct from the home with optimal intensity. This optimal situation is related to the noise dependent relaxation time. It is uniquely defined for all {\alpha} and distinguishes between the stochastic dynamics before and after its value. For times large compared to this we derive the corresponding Smoluchowski equation and find diffusive spreading of searcher in the space. As result of its simplicity the model aims to reproduce the local search behavior of simple units during their exploration of surrounding space and their quasi-periodic return to a home.