Initialising PSO with randomised low-discrepancy sequences: the comparative results

In this paper, we investigate the use of some well-known randomised low-discrepancy sequences (Halton, Sobol, and Faure sequences) for initializing particle swarms. We experimented with the standard global-best particle swarm algorithm for function optimization on some benchmark problems, using randomised low-discrepancy sequences for initialisation, and the results were compared with the same particle swarm algorithm using uniform initialisation with a pseudo-random generator. The results show that, the former initialisation method could help the particle swarm algorithm improve its performance over the latter on the problems tried. Furthermore the comparisons also indicate that the use of different randomised low-discrepancy sequences in the initialisation phase could bring different effects on the performance of PSO.