Authors: Koichiro Sugihara; Naohiro Hayashibara
Addresses: Graduate School of Frontier Informatics, Kyoto Sangyo University, Kyoto, Japan ' Faculty of Information Science and Engineering, Kyoto Sangyo University, Kyoto, Japan
Abstract: Lévy walk has attracted attention for its search efficiency. Homesick Lévy 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 Lévy walk called Nomadic Lévy walk and analyse the behaviour of the algorithm regarding the cover ratio on unit disk graphs. We also show the comparison of Nomadic Lévy walk and Homesick Lévy 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 Lévy 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.
Keywords: random walk; Lévy walk; target exploration; blind search; unit disk graphs; autonomic computing; bio-inspired algorithms.
International Journal of Grid and Utility Computing, 2020 Vol.11 No.2, pp.221 - 229
Received: 25 Aug 2018
Accepted: 12 Jan 2019
Published online: 03 Mar 2020 *