An Improved Intelligent Optimization Algorithm for Optimization Models in Intelligent Internet of Things

The intelligent Internet of Things (IoT), as an important component of the new generation of information technology, is profoundly changing the way humans produce and live. However, with the rapid increase in the number of IoT devices and the complexity of application scenarios, the optimization problems they face are becoming increasingly prominent. Researching optimization problems in intelligent IoT is of great significance for improving system performance, reducing operating costs, and promoting the application of new technologies. Therefore, this article proposes an improved intelligent optimization algorithm that combines particle swarm optimization (PSO) and artificial fish swarm algorithm (ASFA) to solve server location optimization, communication optimization, and industrial IoT scheduling optimization problems in the intelligent Internet of Things (IoT). This hybrid algorithm fully utilizes the global search capability of PSO and the local optimization characteristics of ASFA, and can efficiently find the optimal solution in complex IoT optimization problems. The experimental results show that the hybrid algorithm significantly reduces latency and energy consumption in server location optimization, improves network throughput and resource utilization in communication optimization, and effectively reduces task completion time and resource conflicts in industrial IoT scheduling optimization.