A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems

Optimal assignment of a meta-task in heterogeneous computing systems is NP-complete in the general case. Therefore, heuristic approaches must be employed to find good solutions within a reasonable time. We propose a novel discrete particle swarm optimization (DPSO) algorithm for this problem. Firstly, to make particle swarm optimization algorithm more suitable for solving task assignment problems, particles are represented as integer vectors and a new position update method is developed based on discrete domain. Secondly, an effective variable neighborhood descent algorithm is applied to emphasize exploitation. In addition, migration mechanism is introduced with the hope to escape from possible local optimum and to balance the exploration and exploitation. Computational simulations and comparisons based on a set of benchmark instances indicate that the proposed DPSO algorithm is a viable approach for the task assignment problem.