Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates

Daniel Stolfi, Matthias R. Brust, Grégoire Danoy, P. Bouvry
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引用次数: 7

Abstract

In this paper we present a Predator-Prey approach to enhance the protection of a restricted area using a swarm of Unmanned Aerial Vehicles (UAV). We have chosen the CACOC (Chaotic Ant Colony optimisation for Coverage) mobility model for the UAVs and a new model for intruders based on attractive and repulsive forces. After proposing a number of parameters for each mobility model, we have conducted a competitive optimisation of them (Predators and Preys), to achieve a more robust configuration improving the success rate of UAVs when detecting intruders. We have optimised three case studies by performing 30 independent runs of our competitive coevolutionary genetic algorithm and conducted a number of master tournaments using the best specimens obtained for each case study.
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提高入侵者检测率的无人机群竞争演化
在本文中,我们提出了一种利用无人机群增强限制区域保护的捕食者-猎物方法。我们选择了无人机的caco(混沌蚁群优化覆盖)机动性模型和基于吸引力和排斥力的入侵者新模型。在为每个机动性模型提出了一些参数之后,我们对它们(掠食者和猎物)进行了竞争性优化,以实现更强大的配置,提高无人机在探测入侵者时的成功率。我们对三个案例研究进行了优化,对竞争性协同进化遗传算法进行了30次独立运行,并利用每个案例研究获得的最佳样本进行了多次大师赛。
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