The advantage of L\'evy strategies in intermittent search processes

TL;DR

This paper explores how combining Levy distributed relocations with intermittent search strategies enhances target finding efficiency by reducing oversampling, especially in scenarios with rare targets.

Contribution

It introduces a novel combined search model integrating Levy jumps with intermittent behavior, demonstrating improved search efficiency over traditional methods.

Findings

Levy relocations reduce oversampling in search processes.

Combining Levy jumps with intermittency optimizes search for rare targets.

The model outperforms classical strategies in specific search scenarios.

Abstract

Search strategies based on random walk processes with long-tailed jump length distributions (Levy walks) on the one hand and intermittent behavior switching between local search and ballistic relocation phases on the other, have been previously shown to be beneficial in stochastic target finding problems. We here study a combination of both mechanisms: an intermittent process with Levy distributed relocations. We demonstrate how Levy distributed relocations reduce oversampling and thus further optimize the intermittent search strategy in the critical situation of rare targets.