A novel discrete differential evolution algoritnm for task scheduling in heterogeneous computing systems

Task scheduling is one of the core steps to effectively exploit the capabilities of distributed heterogeneous computing systems. In this paper, a novel discrete differential evolution (DDE) algorithm is presented to address the task scheduling problem. The encoding schemes and the adaptation of classical differential evolution algorithm for dealing with discrete variables are discussed as well as the technique needed to handle boundary constraints. The performance of the proposed DDE algorithm is showed by comparing it with a genetic algorithm, which is a well-known population-based probabilistic heuristic, on a large number of randomly generated instances. Experimental results indicate that the proposed DDE algorithm has generated better results than GA in terms of both solution quality and computational time.