An Analysis of Nonlinear Acceleration Coefficients Adjustment for PSO

Linear acceleration coefficients adjustment had been widely used in particle swarm optimization (PSO). In this paper, a novel nonlinear strategy is developed, where the acceleration coefficients including both cognitive component and social component are adjusted nonlinearly to improve the optimization performance within a reasonable iteration times. Furthermore, the novel adjustment is deeply analyzed by experimental simulations based on four standard test functions. The results confirm the validity of the nonlinear parameter adjustment method in terms of the balance between convergence rate and optimization accuracy.

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

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

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

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

[5]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2011, IEEE Trans. Evol. Comput..

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

[8]  Mohammad Reza Meybodi,et al.  A note on the learning automata based algorithms for adaptive parameter selection in PSO , 2011, Appl. Soft Comput..

[9]  LI Rong-jun.  Experimental Analysis of Acceleration Coefficient in Particle Swarm Optimization Algorithm , 2010 .

[10]  Gang Wang,et al.  Nonlinear PSO—Convergence analysis and parameter adjustment schemes , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[11]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .