Adaptive parameter selection scheme for PSO: A learning automata approach

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting. As modifying a single parameter may result in a large effect. In this paper, we propose a new a new learning automata-based approach for adaptive PSO parameter selection. In this approach three learning automata are utilized to determine values of each parameter for updating particles velocity namely inertia weight, cognitive and social components. Experimental results show that the proposed algorithms compared to other schemes such as SPSO, PSO-IW, PSO TVAC, PSO-LP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better local minima. In addition, proposed algorithms converge to stopping criteria significantly faster than most of the PSO algorithms.

[1]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[2]  Andries P. Engelbrecht,et al.  Effects of swarm size on Cooperative Particle Swarm Optimisers , 2001 .

[3]  Mohammad Reza Meybodi,et al.  Call Admission Control in Cellular Mobile Networks: A Learning Automata Approach , 2002, EurAsia-ICT.

[4]  James Kennedy,et al.  The Behavior of Particles , 1998, Evolutionary Programming.

[5]  Roberto Battiti,et al.  The gregarious particle swarm optimizer (G-PSO) , 2006, GECCO '06.

[6]  David B. Fogel,et al.  A Note on the Empirical Evaluation of Intermediate Recombination , 1995, Evolutionary Computation.

[7]  Suganthan.  [IEEE 1999. Congress on Evolutionary Computation-CEC99 - Washington, DC, USA (6-9 July 1999)] Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) - Particle swarm optimiser with neighbourhood operator , 1999 .

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

[9]  Junyan Wang,et al.  Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization , 2011, ICSI.

[10]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

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

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[14]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

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

[16]  Riccardo Poli,et al.  Exploring extended particle swarms: a genetic programming approach , 2005, GECCO '05.

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

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

[19]  Mohammad Reza Meybodi,et al.  An adaptive call admission algorithm for cellular networks , 2005, Comput. Electr. Eng..

[20]  Chongzhao Han,et al.  Adaptive Particle Swarm Optimization with Feedback Control of Diversity , 2006, ICIC.

[21]  Andreas König,et al.  Local Parameters Particle Swarm Optimization , 2006, 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06).

[22]  Mohammad Reza Meybodi,et al.  A Note on Learning Automata Based Schemes for Adaptation of BP Parameters , 2000, IDEAL.

[23]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[24]  Mohammad Reza Meybodi,et al.  A learning automata-based algorithm for determination of the number of hidden units for three-layer neural networks , 2009, Int. J. Syst. Sci..

[25]  B. John Oommen,et al.  Continuous Learning Automata Solutions to the Capacity Assignment Problem , 2000, IEEE Trans. Computers.

[26]  P. S. Sastry,et al.  Varieties of learning automata: an overview , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[27]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

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