Cooperative multi-task assignment modeling of UAV based on particle swarm optimization - Zhou, Xiaoming; Yang, Kun (Intelligent decision technologies, 2024)
Unmanned Ariel Vehicles (UAVs) are interconnected to perform specific tasks through self-routing and air-borne communications. The problem of automated navigation and adaptive grouping of the vehicles results in improper task completion and backlogs. To address this issue, a Particle Swarm Optimization-dependent Multi-Task Assignment Model (PSO-MTAM) is introduced in this article. The swarms are initialized for the available linear groups towards the destination. This article addressed the subject of UAVs using a multi-task assignment paradigm to increase task completion rates and handling efficiency. The different swarm stages are verified for the task progression, resulting in completion at the final stage. In this completion process, the first local best solution is estimated using the completion and assignment rate of a single task. The second local best solution relies on reaching the final stage. The global solution is identified depending on the convergence of the above solutions in task progression and handling density. The swarm positions are immediately identified, and the synchronous best solutions generate the final global best. The backlog-generating solutions are revisited by reassigning or re-initializing the swarm objects. The proposed model’s performance is analyzed using task handling rate, completion ratio, processing time, and backlogs. Improving the handling rate is essential for this validation, necessitating solution and position updates from the intermediate UAVs. With varying task densities and varying degrees of convergence, the iterations continue until completion. There is an 11% increase in the task handling rate and a 12.02% increase in the completion ratio with the suggested model. It leads to a 10.84% decrease in processing time, a 9.91% decrease in backlogs, and a 12.7% decrease in convergence cost.