Investigation of Grey Wolf Algorithm for Solving Heterogeneous Unmanned Aerial Vehicle Task Assignment Problem

In recent years, Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in various applications, including surveillance, monitoring, and delivery services. Efficiently assigning tasks to UAVs while optimizing multiple objectives such as distance travelled, energy consumption, and task completion time presents a complex optimization problem. To address this challenge, the approach put forward is a Multi-Objective Grey Wolf Algorithm (MOGWA) specifically tailored for solving UAV task assignment problems in a two-dimensional (2D) environment. The MOGWA is an extension of the classic Grey Wolf Algorithm (GWA), which draws inspiration from the social behaviour and hunting strategies of grey wolves. In the proposed MOGWA, UAVs are represented as individual grey wolves, and their movements are guided by principles of hierarchical leadership within a wolf pack. This allows for a balance between exploration and exploitation of the solution space, enabling efficient search for optimal task assignments. Experimental evaluations demonstrate the effectiveness of the MOGWA in finding high-quality solutions for UAV task assignment problems in 2D environments. The proposed algorithm offers a promising framework for addressing real-world challenges in UAV mission planning and optimization, particularly in scenarios where multiple conflicting objectives need to be considered simultaneously.