论文标题
运动编码的粒子群优化用于使用UAV的移动目标搜索
Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs
论文作者
论文摘要
本文介绍了一种新型算法，称为运动编码的粒子群优化（MPSO），用于寻找无人驾驶汽车（UAV）的移动目标。从贝叶斯理论中，搜索问题可以转换为代表检测目标概率的成本函数的优化。在这里，提出的MPSO是通过将搜索轨迹编码为一系列无人机运动路径来解决该问题的发展，这些运动路径在PSO算法中的产生中演变而来。这种运动编码的方法允许保留群体的重要特性，包括认知和社会连贯性，从而导致更好的解决方案。与原始PSO相比，具有现有方法的广泛模拟的结果表明，与原始PSO相比，提出的MPSO将检测性能提高了4.71倍，并且还优于其他最先进的元智能优化算法，包括人工蜜蜂殖民地（包括人工蜂群（ABC），蚂蚁群体优化（ACO）（ACO）（ACO）（DE），GAG（ACO），差异（ACO），差异（ga）在大多数搜索方案中，算法（TSA）。在不同场景中搜索动态目标时，已经使用了实际无人机进行实验，以证明在实际应用中的MPSO优点。
This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24\% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.
