【摘要】 针对目前高性能计算任务调度策略利用率低、负载不均衡等问题，设计一种基于遗传-蚁群算法的高性能计算任务调度算法(GA-ACO)。GA-ACO分为两个阶段，第一阶段通过遗传算法缩小空间快速搜索到优秀解，紧接着将其转化为蚁群算法的初始信息素；第二阶段提出一种基于蚁群信息素的全局更新策略对收敛速度做出优化。实验分析表明，与蚁群算法和遗传算法相比，该算法缩短了任务完成时间，降低了节点负载率。更多还原

【Abstract】 Aimed at the problems of low utilization rate and unbalanced load of current high performance computing task scheduling strategies, a high-performance computational task scheduling algorithm based on genetic ant colony optimization(GA-ACO) is designed. GA-ACO was divided into two stages. In the first stage, the genetic algorithm was used to narrow the space and quickly find the excellent solution, and then it was transformed into the initial pheromone of ant colony algorithm. In the second stage, a global update strategy based on ant colony pheromone was proposed to optimize the convergence speed. Experimental analysis shows that compared with ant colony algorithm and genetic algorithm, this algorithm shortens the task completion time and reduces the node load rate.更多还原