Time-varying PSO - convergence analysis, convergence-related parameterization and new parameter adjustment schemes

In this paper, a formal convergence analysis of the conventional PSO algorithms with time-varying parameters is presented. Based on this analysis, a new convergence-related parametric model for the conventional PSO is introduced. Finally, several new schemes for parameter adjustment, providing significant performance benefits, are introduced. Performance of these schemes is empirically compared to conventional PSO algorithms on a set of selected benchmarks. The tests prove effectiveness of the newly introduced schemes, especially regarding their ability to efficiently explore the search space.

[1]  L. Hogben Handbook of Linear Algebra , 2006 .

[2]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[3]  Shiyuan Yang,et al.  Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm , 2007, Inf. Process. Lett..

[4]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

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

[6]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[7]  E. Ozcan,et al.  Particle swarm optimization: surfing the waves , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  M. Senthil Arumugam,et al.  On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems , 2008, Appl. Soft Comput..

[9]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[10]  Chilukuri K. Mohan,et al.  Analysis of a simple particle swarm optimization system , 1998 .

[11]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).