Efficient Allocation of System Basic Resources Based on Chaotic Particle Swarm Optimization Algorithm

In this study, we propose an efficient allocation framework for system resource scheduling by integrating chaotic particle swarm optimization (PSO). Three approaches were compared: the polling algorithm, the standard PSO algorithm, and the chaotic PSO algorithm. We conducted comparative experiments to analyze their performance in terms of total task execution time, prediction error of execution time, resource scheduling complexity, balance, and overall resource consumption levels. The experimental results demonstrated that the chaotic PSO algorithm outperformed the other two algorithms across all tested metrics. This research aims to develop a dynamic resource allocation mechanism capable of adjusting resources adaptively based on system load fluctuations. By incorporating chaotic optimization into PSO, we enhanced both resource allocation efficiency and multi-resource load balancing. The findings confirm the superior performance of chaotic PSO in resource-constrained scheduling environments.