Refinement of Collaborative Multitasking and Multi-Objective Allocation in Heterogeneous UAV Swarms An Approach Informed by Decomposition Learning and Particle Swarm Optimization

The utilization of Unmanned Aerial Vehicle(UAV) swarms has witnessed a remarkable rise across diverse domain, accompanied by an escalating complexity of challenges. This study addresses these challenges by concurrently considering task rescue value of and UAV type, while also introduce ground vehicles to UAVs recharging or supplies supplementation. The primary objective of this paper is to construct a multi-objective multi-task allocation model for UAV swarms, with the aim of minimizing rescue penalties and resource utilization rates. To tackle this model, the study proposes a Multi-Objective Particle Swarm Optimization based on Decomposition Learning (MPSO-DL). The MPSO-DL algorithm incorporates several innovative features. Firstly, it employs an initialization method that combines clustering and greedy selection techniques to enhance the quality of the initial population; secondly, a particle updating strategy based on solution decomposition learning is devised to expedite the convergence rate of the algorithm; Finally, a local search strategy, leveraging a knowledge base, is developed to increase the probability of the population escaping local optima. Through simulation experiments under three different scenarios, the proposed algorithm is rigorously evaluated. The results demonstrate that the proposed algorithm's ability to yield a set of task allocation strategies characterized by superior convergence and distribution.