RT Journal Article
T1 Ant colony optimization for multi-UAV minimum time search in uncertain domains
A1 Perez-Carabaza, Sara
A1 Besada Portas, Eva
A1 López Orozco, José Antonio
A1 Cruz García, Jesús Manuel De La
AB This paper presents a new approach based on ant colony optimization (ACO) to determine the trajectories of a fleet of unmanned air vehicles (UAVs) looking for a lost target in the minimum possible time. ACO is especially suitable for the complexity and probabilistic nature of the minimum time search (MTS) problem, where a balance between the computational requirements and the quality of solutions is needed. The presented approach includes a new MTS heuristic that exploits the probability and spatial properties of the problem, allowing our ant based algorithm to quickly obtain high-quality high-level straight segmented UAV trajectories. The potential of the algorithm is tested for different ACO parameterizations, over several search scenarios with different characteristics such as number of UAVs, or target dynamicsand location distributions. The statistical comparison against other techniques previously used for MTS( ad hoc heuristics, cross entropy optimization, bayesian optimization algorithm and genetic algorithms) shows that the new approach outperforms the others. (C) 2017 Elsevier B.V. All rights reserved.
PB Elsevier
SN 1568-4946
YR 2017
FD 2017
LK https://hdl.handle.net/20.500.14352/94255
UL https://hdl.handle.net/20.500.14352/94255
LA eng
NO Sara Perez-Carabaza, Eva Besada-Portas, Jose A. Lopez-Orozco, Jesus M. de la Cruz, Ant colony optimization for multi-UAV minimum time search in uncertain domains, Applied Soft Computing, Volume 62, 2018, Pages 789-806, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2017.09.009.
NO El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital.
NO AIRBUS
DS Docta Complutense
RD 15 ago 2025