A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms
Daniel H. Stolfi, Matthias R. Brust, Grégoire Danoy, Pascal Bouvry · 2020
This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently.