A Novel Discrete Particle Swarm Optimization Algorithm for Job Scheduling in Grids

Abstract

The emerging computational grid infrastructure consists of heterogeneous resources in widely distributed autonomous domains, which makes job scheduling even more challenging. In this paper, we propose a novel discrete variant of particle swarm optimization (PSO) algorithm to solve job scheduling problem. In the proposed algorithm, each particle is encoded in a natural number vector and an efficient approach is developed to move particles in the solution space. The algorithm preserves the generality of standard PSO and can be implemented easily. To verify the proposed algorithm, comparisons with a continuous PSO algorithm and a genetic algorithm are made. Computational results show that the proposed discrete PSO algorithm is very competitive.