The dramatic increase in the capabilities and availability of autonomous ground and aerial tools introduces safety and security challenges, particularly in protecting strategic infrastructures. In this context, the interception of multiple mobile threats, aiming to invade restricted spaces of such infrastructures is an important topic. This paper focuses on the problem of path planning for intercepting multiple aerial targets by a swarm of UAVs. 3D path planning for interception of moving targets is a challenging task, in particular when the interception is performed by a swarm of UAVs, as there are multiple kinematic and dynamic constraints. The aim is first to allocate targets to the individual UAVs (task assignment) and to construct a 3D path for each one. Many algorithms have been recognized as noble schemes for solving this kind of problems based on Swarm Intelligence (SI), many of them are based on biological systems such as particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), bat-inspired algorithm (BA), etc. The paper presents a comprehensive review of SI algorithms centered on the problems related to 3D path planning for target interception by a swarm of UAVs. It also focuses on the improvement of existing SI algorithms for better path optimization. A comprehensive investigation for each algorithm is presented by analyzing its merits and demerits in the context of target interception. This broad review is an outline for scholars and professionals in the field of the swarm of UAVs.

自主地面和空中工具的能力和可用性的急剧增加带来了安全和安保挑战，特别是在保护战略基础设施方面。在这种情况下，拦截多种移动威胁，旨在侵入此类基础设施的受限空间是一个重要课题。本文重点研究无人机群拦截多个空中目标的路径规划问题。用于拦截移动目标的 3D 路径规划是一项具有挑战性的任务，特别是当拦截由一群无人机执行时，因为存在多个运动学和动态约束。目标是首先将目标分配给各个 UAV（任务分配）并为每个 UAV 构建一个 3D 路径。等等。本文对 SI 算法进行了全面回顾，重点关注与 3D 路径规划相关的问题，以实现一群无人机的目标拦截。它还侧重于改进现有的 SI 算法以实现更好的路径优化。通过分析其在目标拦截上下文中的优缺点，对每种算法进行了全面的研究。这篇广泛的综述是为无人机群领域的学者和专业人士提供的大纲。