Optimizing Resource Utilization in Consumer Electronics Networks Through an Enhanced Grey Wolf Optimization Algorithm With UAV Collaboration
Xiaoteng Yang, Jie Feng, Lei Liu, Qingqi Pei · IEEE Transactions on Consumer Electronics · 2025
With the continuous advancement of electronic technology, consumer electronic devices (CEs) have become indispensable to people’s daily lives. However, with the increasing complexity of device functions and the rapid growth of data size, efficiently utilizing resources and improving computing performance has become an important issue that needs to be urgently addressed in consumer electronic networks. Existing research often finds it difficult to effectively balance global optimization and local fine-tuning when facing dynamic and complex environments, resulting in poor resource scheduling efficiency and, to a certain extent, restricting the system’s scalability. To address these problems, this paper proposes an adaptive enhanced grey wolf optimization(AEGWO) algorithm that integrates the skyhawk hunting strategy to improve the collaborative computing capabilities of the UAV network. The algorithm introduces a new search mechanism, a nonlinear convergence factor. It combines the individual optimal learning method to more effectively balance global exploration and local optimization, thereby improving resource scheduling efficiency and the system’s adaptability. Simulation experimental results show that AEGWO performs better than existing algorithms on standard test functions and achieves significant resource optimization effects in UAV-assisted computing scenarios. These results verify the effectiveness of AEGWO in improving system efficiency and optimizing resource allocation and provide a new solution for electronic device resource management in complex computing environments.