基于信息熵的混沌遗传算法求解网格工作流调度问题

网格发展的主要思想是有效的利用分布在世界各地的计算资源。而在网格环境下,是通过很多相互依赖的任务来描述作业的,这让工作流调度面临巨大的挑战。在本文中,提出了一个改进型的混沌遗传演算法来解决在工作流应用程序中的调度优化问题,它利用信息熵的概念动态调整了交叉和变异概率,优化了传统的遗传算法,并最终通过实验证明了算法的有效性。

To Solve the Grid Workflow Scheduling Problem Based on the Entropy of Chaos Genetic Algorithm

The main ideas of the grid de;eelopment is using computing resources effective which distributed in all over the world. In grid environment, the work is described by many interdependent tasks, so the workflow scheduling will meet enormous challenges. This article puts forward an improved chaos genetic algorithm to solve the problem of scheduling optimization in the workflow application. It uses the concept of entropy dynamically adjust crossover and mutation probability to optimize the traditional genetic algorithm, and finally the experimental results shows the effectiveness of the algorithm.