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Multiple UAVs mission assignment based on modified Pigeon-inspired optimization algorithm

机译:基于改进的鸽子启发优化算法的多无人机任务分配

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Unmanned aerial vehicles (UAVs) have shown their superiority for applications in performing military and civilian missions. In which, multiple UAVs mission assignment is becoming more important for today's military activities. So far, there have been many bio-inspired computation algorithms for solving multiple UAVs mission assignment problems, including particle swarm optimization (PSO), differential evolution algorithm (DE) and so on. However, deficiencies of these approaches exist inevitably, which cannot satisfy the requirements of dynamic mission assignment. In this paper, a new UAV assignment model focusing on the energy consumption of UAV is brought up which can be easily applied to intelligence algorithms. Meanwhile, we propose a new approach by applying the modified Pigeon-Inspired Optimization (PIO) algorithm to sovle the multiple UAVs mission assignment problem. The simulation results show that the modified PIO algorithm is more effective when compared with other state-of-the-art algorithms in addressing mission assignment problem for multiple UAVs.
机译:无人飞行器(UAV)已显示出其在执行军事和民用任务中的优势。其中,多种无人机的任务分配对于当今的军事活动变得越来越重要。到目前为止,已有许多生物启发性的计算算法可以解决多种无人机任务分配问题,包括粒子群优化(PSO),差分进化算法(DE)等。但是,这些方法的缺陷不可避免地存在,不能满足动态任务分配的要求。本文提出了一种针对无人机能耗的新型无人机分配模型,该模型可以很容易地应用于智能算法。同时,我们提出了一种新的方法,即通过应用改进的“鸽子启发式优化”(PIO)算法解决多个无人机的任务分配问题。仿真结果表明,与其他先进算法相比,改进后的PIO算法在解决多架无人机的任务分配问题上更为有效。

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