Title: Many-objective optimisation-based optimal drone deployment for agricultural zone

Authors: Hassina Ait Issad; Rachida Aoudjit; Malika Belkadi; Joel J.P.C. Rodrigues

Addresses: LARI Lab, University Mouloud Mammeri of Tizi-Ouzou, Algeria ' LARI Lab, University Mouloud Mammeri of Tizi-Ouzou, Algeria ' LARI Lab, University Mouloud Mammeri of Tizi-Ouzou, Algeria ' Federal University of Piauí, Juiz de Fora 36036-900, Brazil; Instituto de Telecomunicações 1049-001 Lisbon, Portugal

Abstract: Monitoring using drones is not just a civilian and military task, but it also concerns the agricultural sector, where it can play an important role in the context of smart agriculture. It seems to be a very valuable tool in the future. However, the optimal deployment of a set of monitoring drones is a very challenging problem; it is a NP-Hard problem. In this paper, the deployment problem has been modelled as a constrained many-objective optimisation problem. Powerful heuristics, namely multi-objective artificial bee colony (MOABC), multi-objective particle swarm optimisation (MOPSO), non-dominated sorting genetic algorithm II (NSGA II), strength Pareto evolutionary algorithm II (SPEA II) and non-dominated sorting genetic algorithm III (NSGA III) are used to find the optimal deployment strategy with four goals: minimising energy consumption, maximising total coverage, maintaining connectivity and minimising overlaps. A comparative study was carried out and the results showed that the SPEA II, NSGA III and NSGA II algorithms have better convergence and maintain good diversity than the other algorithms.

Keywords: drones deployment; coverage problem; unmanned aerial vehicle; UAV; many-objective optimisation; Pareto front.

DOI: 10.1504/IJCNDS.2021.111632

International Journal of Communication Networks and Distributed Systems, 2021 Vol.26 No.1, pp.76 - 98

Received: 15 Aug 2019
Accepted: 19 Dec 2019
Published online: 04 Dec 2020 *

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