Empirical assessment of the effects of update synchronization in Particle Swarm Optimization

Despite considerable popularity, the mechanisms that govern the behavior of Particle Swarm Optimization (PSO) are still a subject of research . Regarding communication between particles, for example, many authors ha ve discussed the effects of swarm topology, but few have studied the dynamic s of the information exchange among particles. In this paper we show that a sync hronous update of the social attractors, which is necessary when parallel versio ns of PSO are implemented, may influence the effectiveness of the algorithm. To do s we compare the synchronous and asynchronous variants of PSO on a sta nd rd benchmark. The results show that the ‘global best’ topology is sensitive to the po licy update, especially in the presence of high-dimensional search spaces . In ontrast, sparsely-connected topologies seem to be much less sensitive to synchr onization.

[1]  Anne Auger,et al.  Performance evaluation of an advanced local search evolutionary algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[2]  V. Miranda.  Evolutionary Algorithms with Particle Swarm Movements , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[3]  A. Groenwold,et al.  Comparison of linear and classical velocity update rules in particle swarm optimization: notes on diversity , 2007 .

[4]  Raymond Ros,et al.  Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup , 2009 .

[5]  Anne Auger,et al.  Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions , 2009 .

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[8]  Mihai Oltean,et al.  What else is the evolution of PSO telling us , 2008 .

[9]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[10]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  N. Hansen,et al.  PSO Facing Non-Separable and Ill-Conditioned Problems , 2008 .

[12]  Maurice Clerc,et al.  Back to random topology , 2007 .