Target exploration by Nomadic Lévy walk on unit disk graphs

Levy walk has attracted attention for its search efficiency. Homesick Levy walk is a family of random walks whose encounter probability of one another is similar to the one of human behaviour. However, its homing behaviour limits the search area of each agent. In this paper, we propose a variant of Homesick Levy walk called Nomadic Levy walk and analyse the behaviour of the algorithm regarding the cover ratio on unit disk graphs. We also show the comparison of Nomadic Levy walk and Homesick Levy walk regarding the target search problem. Our simulation results indicate that the proposed algorithm is significantly efficient for sparse target detection on unit disk graphs compared to Homesick Levy walk, and it also improves the cover ratio. Moreover, we analyse the impact of the movement of the sink (home position) on the efficiency of the target exploration.