A Review of the Application of Optimization Algorithms Nature Inspired in the Design of Flight Paths

In this article, an overview of the application of optimization algorithms inspired by nature in the design of the flight path has been discussed. The significant increase in the capabilities and availability of independent ground and air tools makes safety and security challenges especially important in the protection of strategic infrastructure. In this context, the detection of multiple mobile threats with the aim of invading the limited spaces of these infrastructures is an important issue. This paper focuses on the trajectory planning problem for the interception of multiple aerial targets by a group of UAVs. 3D trajectory planning for the tracking of moving targets is a challenging task, especially when the tracking is performed by a crowd of UAVs, because there are numerous motion and dynamic constraints. The goal is first to assign targets to individual drones (task assignment) and create a 3D path for each one. Many algorithms are known as the main schemes to solve this kind of problem based on collective intelligence; many of them are based on biological systems such as particle swarm optimization, ant colony optimization, artificial bee colony optimization, bat-inspired algorithm, etc. This article presents a comprehensive review of collective intelligence algorithms focusing on 3D path planning problems for target tracking by a group of UAVs. It also focuses on improving existing collective intelligence algorithms for more optimal and correct path optimization. A comprehensive review of each algorithm with an analysis of its advantages and disadvantages in the context of target tracking is also provided. This extensive review is the outline for researchers and practitioners in the field of mass UAVs.