A High Efficiency Task Allocation Method Based on Adaptive Hybrid Particle Swarm Optimization for Intelligent Unmanned Wireless Sensor Networks

In recent years, with the continuous application of intelligent unmanned technology, network optimization of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has emerged as a research hotspot. However, due to the limitations imposed by sensor size, the sensing capabilities of small wireless sensors in IUWSN s are restricted. To achieve higher network performance, it is crucial to devise suitable and efficient task allocation schemes. Therefore, a novel Adaptive Hybrid Particle Swarm Optimization algorithm (AHPSO) is proposed for efficient task allocation in IUWSN s. Firstly, AHPSO significantly enhances the efficiency of task distribution in IUWSN s, thereby markedly improving network benefits. Secondly, the new adaptive mechanism, by dynamically adjusting algorithm parameters, greatly increases the diversity of population solutions and accelerates the convergence speed of the algorithm. Additionally, a new simulated annealing strategy created in this study effectively prevents the algorithm from falling into local optima. Finally, extensive simulation experiments were conducted comparing AHPSO with Genetic Algorithms (GA) and Simulated Annealing (SA). The results demonstrate that AHPSO markedly outperforms other methods in enhancing task allocation efficiency and overall network performance of IUWSNs.