Particle Swarm Optimization: Iteration Strategies Revisited

Abstract

Particle swarm optimization (PSO) is an iterative algorithm, where particle positions and best positions are updated per iteration. The order in which particle positions and best positions are updated is referred to in this paper as an iteration strategy. Two main iteration strategies exist for PSO, namely synchronous updates and asynchronous updates. A number of studies have discussed the advantages and disadvantages of these iteration strategies. Most of these studies indicated that asynchronous updates are better than synchronous updates with respect to accuracy of the solutions obtained and the speed at which swarms converge. This study provides evidence from an extensive empirical analysis that current opinions that asynchronous updates result in faster convergence and more accurate results are not true.