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

机译:基于客观优化的农业区的最优无人机部署

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摘要

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.
机译:使用无人机监测不仅仅是民用和军事任务,而且还涉及农业部门,在智能农业的背景下它可以发挥重要作用。未来似乎是一个非常有价值的工具。但是,一组监测无人机的最佳部署是一个非常具有挑战性的问题;这是一个np难题的问题。在本文中,部署问题已被建模为约束的多目标优化问题。强大的启发式,即多目标人工蜂菌落(MoABC),多目标粒子群优化(MOPSO),非主导的分类遗传算法II(NSGA II),强度帕曲面进化算法II(SPEA II)和非主导分类遗传算法III(NSGA III)用于找到具有四个目标的最佳部署策略:最大限度地减少能量消耗,最大限度地提高总覆盖率,保持连接和最小化重叠。进行了比较研究,结果表明,SPEA II,NSGA III和NSGA II算法具有更好的收敛性,并保持比其他算法更好的多样性。

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