摘要： 针对网格任务调度问题，提出一种基于改进混合蛙跳算法的网格任务调度策略。通过引入遗传算子增加对局部极值的扰动，以避免陷入局部最优，同时借鉴粒子群优化算法中粒子飞行经验，对青蛙移动策略进行优化。实验结果表明，该策略高效合理，能够缩减执行任务的时间跨度，并提高最优解的质量。

关键词: 网格, 任务调度, 混合蛙跳算法, 遗传算法, 粒子群优化算法

Abstract: This paper proposes an improved Shuffled Frog Leaping Algorithm(SFLA) for grid task schedule. The algorithm is based on traditional SFLA. It introduces the genetic operators to increase relative extremum disturbance to avoid falling into a local optimum, and the frog leaping strategy is optimized by learn from the particle flying experience of Particle Swarm Optimization(PSO) algorithm. Experimental results prove the strategy has better performance. It can be faster to get a high-quality optimal solution.

Key words: grid, task schedule, Shuffled Frog Leaping Algorithm(SFLA, Genetic Algorithm(GA), Particle Swarm Optimization(PSO) algorithm

中图分类号: 

• TP301.6