A hybrid differential evolution algorithm for meta-task scheduling in grids

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous resources in the grid. This paper presents a new hybrid differential evolution (HDE) algorithm for finding an optimal or near-optimal schedule within reasonable time. The encoding scheme and the adaptation of classical differential evolution algorithm for dealing with discrete variables are discussed. A simple but effective local search is incorporated into differential evolution to stress exploitation. The performance of the proposed HDE algorithm is showed by being compared with a genetic algorithm (GA) on a known static benchmark for the problem. Experimental results indicate that the proposed algorithm has better performance than GA in terms of both solution quality and computational time, and thus it can be used to design efficient dynamic schedulers in batch mode for real grid systems.