Simulation of Embedded Multi task Scheduling Model Based on Particle Swarm Optimization

With the widespread application of embedded systems in various industries, how to effectively schedule multiple tasks has become a key issue. Multi task scheduling is not only related to system performance, but also to real-time performance and stability, especially in resource constrained embedded environments. This article explores the possibility and advantages of introducing PSO into embedded multitask scheduling. PSO, as a global optimization algorithm that simulates the social behavior of bird flocks, has demonstrated its optimization effectiveness in multiple fields. We propose a scheduling model based on PSO, aiming to optimize the execution order of tasks to meet real-time requirements while improving system performance. Through simulation experiments, we evaluated the effectiveness of the proposed model and compared it with traditional scheduling strategies. The results show that the scheduling strategy based on PSO can provide better system throughput and response time while meeting real-time constraints. This study provides a new perspective for multitask scheduling in embedded systems, showcases the potential application of PSO in this field, and lays the foundation for future research and application.