Intelligent optimization for multiprocessor systems: hybrid algorithmic strategies for scheduling and load balancing

Abstract

Efficient scheduling and load balancing are essential for optimizing performance in multiprocessor systems. This study proposes a novel hybrid algorithm that integrates beam search and differential evaluation techniques within the domain of artificial intelligence (AI) to address these challenges. Our objective is to minimize the operational completion time (OCT), a critical metric for evaluating system performance. Beam search is utilized to explore the solution space effectively, enabling the algorithm to identify promising solutions. Moreover, we employ a differential evaluation approach to assess the quality of candidate solutions and guide the search toward optimal or near-optimal scheduling and load-balancing configurations. By combining these techniques, our hybrid algorithm aims to minimize OCT, thereby enhancing system throughput and resource utilization. Experimental evaluations demonstrate the effectiveness of our approach in achieving improved performance compared to traditional methods. This research contributes to advancing the field of AI in multiprocessor systems optimization, providing practical solutions for real-world deployment in high-performance computing environments.