A Kind of Decay-Curve Inertia Weight Particle Swarm Optimization Algorithm

Based on the research results published in existing relevant references, the basic principles of the standard particle swarm optimization (PSO) algorithm are elaborated and analyzed. To the shortcomings of the standard particle swarm optimization algorithm such as the success rate, number of iterations, running time and the local optimum in the optimization process, a new kind of decay-curve inertia weight Particle Swarm Optimization Algorithm (CPSO) is presented and the astringency analysis is finished. The comparison between the CPSO algorithm and the standard PSO algorithm through the experiment a analysis show that, the CPSO algorithm is apparently better than the standard PSO algorithm both in the convergence speed an convergence precision.

[1]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.

[2]  L Hong The Convergence Analysis of A Class of Evolution Strategies , 1999 .

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

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

[5]  C. Mohan,et al.  Multi-phase generalization of the particle swarm optimization algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[7]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Wenjun Zhang,et al.  Dissipative particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Russell C. Eberhart,et al.  Adaptive particle swarm optimization: detection and response to dynamic systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

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