MCSOA: A Novel High-Reliable Wireless Sensor Networks Routing Optimization Method for Power Internet of Things

In recent years, as the Power Internet of Things continues to advance and improve, the routing requires efficient and reliable optimization methods to ensure stable operation. However, sensor nodes in Wireless Sensor Networks for Power Internet of Things have limited energy and lifespan, making the search for an efficient routing optimization method a critical issue. This paper innovatively proposes a novel high-reliable Wireless Sensor Networks routing optimization method for Power Internet of Things-the Multi-Objective Chaotic Seagull Optimization Algorithm (MCSOA). MCSOA integrates chaotic optimization strategies with the Seagull Optimization Algorithm, enable to escape local optima and improving its global search capability. Additionally, it incorporates multi-objective functions to address various key factors in PSWSN routing, including minimizing transmission loss, balancing load distribution, and enhancing system reliability. To evaluate the effectiveness of the optimization method, extensive new experiments were conducted, accurately reflecting real-world power grid operation scenarios. The experimental findings indicate that MCSOA significantly outperforms traditional algorithms like Genetic Algorithm and Particle Swarm Optimization in terms of efficiency. Specifically, MCSOA cuts down energy use by a minimum of 16.1% and prolongs the lifespan of the PSWSN by at least 15.6%.