Tanvi Iyer
Independent Researcher
India
Abstract
Efficient scheduling in multiprocessor systems is a critical problem for improving system performance, resource utilization, and minimizing execution time. Traditional scheduling algorithms often face challenges handling the complexity and constraints in multiprocessor task assignments. Genetic Algorithms (GAs), inspired by natural selection, provide a robust heuristic approach to solving complex optimization problems. This manuscript presents a detailed study on optimizing scheduling in multiprocessor systems using Genetic Algorithms. The research evaluates the effectiveness of GA-based scheduling compared to classical methods, analyzing performance through simulation and statistical metrics. The results demonstrate significant improvements in makespan reduction and processor utilization. This study reinforces the suitability of Genetic Algorithms for multiprocessor scheduling optimization within the constraints and technology available until 2020.
Keywords
Multiprocessor scheduling, Genetic Algorithm, optimization, makespan, heuristic, resource utilization
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