UAV Path Planning for Data Collection From Wireless Sensor Network With Matrix-Based Evolutionary Computation

Uncrewed aerial vehicles (UAVs) are increasingly employed for data collection in wireless sensor networks (WSNs) owing to their flexibility and real-time operational capabilities. However, effective UAV path planning remains a critical research challenge, requiring the design of optimal routes to efficiently complete data collection in WSNs. This paper introduces a novel constrained UAV data collection model tailored to address real-world challenges in this domain. Traditional mathematical optimization methods often face significant difficulties in derivation and computational complexity. Similarly, classical evolutionary computation (EC) algorithms are limited by their dependence on serial computations, resulting in substantial time costs. To address these issues, we propose a matrix-based differential evolution algorithm (MDE), leveraging matrix index operations to facilitate parallel computation and solve the problem efficiently. Given that existing matrix-based evolutionary computation (MEC) algorithms have limited applications in constrained optimization problems, we further introduce a constraint-guided optimization (CGO) method, enabling the MDE algorithm to inherently support constrained optimization. Experimental results demonstrate that the proposed MDE-CGO outperforms other representative EC methods in optimizing the model of constrained UAV data collection from WSNs. Only our proposed approach successfully optimizes the model to generate feasible UAV paths in all the experiments. Moreover, a computational speed comparison highlights that the MDE-CGO not only delivers superior optimization performance but also achieves high computational efficiency.