Bordón Ruiz, Juan B.López Orozco, José AntonioBesada Portas, Eva2023-06-222023-06-222022-04-051747-777810.1080/17477778.2022.2053311https://hdl.handle.net/20.500.14352/71440©2022 Taylor & Francis LTD This work is supported by the Spanish Ministry of Science and Innovation (MICINN) under AMPBAS (RTI2018-098962- B-C21) and by the Madrid Regional Government under IA-GES-BLOOM-CM (Y2020/TCS-6420). The computation has been supported by the Google Cloud Research Credits program with award GCP19980904.This paper presents a new Cloud-deployable DEVS-based framework for optimising UAV trajectories and sensor strategies in target-search missions. DEVS provides it with a well-established, flexible, and verifiable modelling strategy to include different models for the UAV, sensor, and target dynamics; the target and sensor uncertainty; and the optimising process. Its Cloud deployability speeds up the evaluations/simulations required to optimise this NP-hard problem, which involves computationally heavy models when solving real-world missions. The framework, designed to handle different types of target-search missions, currently optimises, using a multi-objective Genetic Algorithm, free-shape trajectories of multiple UAVs,eqquiped with several static/movable sensors to detect a target within a search area. It is implemented in xDEVS and deployable over a set of containers in the Google Cloud Platform. The results show that our deployment policy speeds up the computation up to 3.35 times, letting the operator simultaneously optimise several search strategies for agiven scenario.engAtribución-NoComercial 3.0 Españajournal articlehttp://dx.doi.org/10.1080/17477778.2022.2053311https://www.tandfonline.com/open access004.8Unmanned aerial vehiclesCooperative-searchGenetic algorithmSimulationOptimizationFrameworkSurveillanceModelfireairSimulation in cloudDiscrete event system specificationModel-Based systems engineeringBayesian searchMulti-objective path planningInteligencia artificial (Informática)1203.04 Inteligencia Artificial