References of "Stolfi, Daniel H."
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See detailCONSOLE: intruder detection using a UAV swarm and security rings
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Swarm Intelligence (2021), 15(3), 205--235

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See detailImproving Pheromone Communication for UAV Swarm Mobility Management
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 13th International Conference on Computational Collective Intelligence (ICCCI 2021) (2021)

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See detailOptimising pheromone communication in a UAV swarm
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in GECCO '21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (2021)

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See detailA competitive Predator–Prey approach to enhance surveillance by UAV swarms
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Applied Soft Computing (2021), 111

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a ... [more ▼]

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a Competitive Coevolutionary Genetic Algorithm (CompCGA) which optimises the parameters of the UAVs (i.e. predators) to maximise the detection of intruders, while the parameters of the intruders (i.e. preys) are optimised to maximise their intrusion success rate. Having chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) as the base mobility model for the UAVs, we propose an improved new version, where its behaviour is modified by identifying and optimising new parameters to improve the overall success rate when detecting intruders. Six case studies have been optimised using simulations by performing 30 independent runs (180 in total) of our CompCGA. Finally, we conducted a series of master tournaments (1,800,000 evaluations) using the best specimens obtained from each run and case study to test the robustness of our proposed approach against unexpected intruders. Our surveillance system improved the average percentage of intruders detected with respect to CACOC by a maximum of 126%. More than 90% of intruders were detected on average when using a swarm of 16 UAVs while CACOC’s detection rates are always under 80% in all cases. [less ▲]

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