基于GSLF-SSA的异构多核处理器任务调度OACSTPCD

To enhance the computational performance of heterogeneous multi-core processor platforms,we propose an improved Sparrow Search Algorithm called the Fusion of Golden Sinusoidal and Levy Flight in Sparrow Search Algorithm(GSLF-SSA)to optimize task scheduling on heterogeneous multi-core processors.Through an analysis of heterogeneous task scheduling,we model these tasks as a Directed Acyclic Graph(DAG)task model.By randomly encoding priorities for these DAG tasks,the GSLF-SSA algorithm achieves a mapping of the solution domain from continuous to discrete,making it more adaptable for heterogeneous multi-core task scheduling.The algorithm iteratively refines its fitness value using the optimal scheduling length of DAG tasks.Experimental comparisons with several widely used heuristic algorithms in the context of heterogeneous task scheduling,including Sparrow Search Algorithm(SSA),an improved task scheduling algorithm based on hybrid optimization strategy(IHSSA),and Artificial Bee Colony Algorithm(ABC),dem-onstrate that GSLF-SSA achieves superior scheduling lengths and shorter scheduling execution time.