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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 8,August 2012
Training RBF Neural Network by hybrid Adaptive Modified Particle Swarm Optimization - Tabu Search algorithm for Function Approximation
Full Text(PDF, )  PP.1380-1387  
Mohammad Alipour Varmazabadi, Ali Solyemani Aiouri
Function Approximation , metaheuristics , neural network , Particle Swarm Optimization , RBF , Tabu Search , gradient descent
This days interests in metaheuristic optimization algorithms have been grown and this algorithms have been used in many fields . Because of this , many algorithms have born or mixed up together in order to get into better results . In this paper a new hybrid AMPSO-TS ( Adaptive Modified Particle Swarm Optimization - Tabu Search ) method is presented for training RBF (Radial Basis Function ) neural network that used to function approximation . This AMPSO-TS method uses the search ability of Tabu Search algorithm to optimize the Adaptive Modified Particle Swarm Optimization to result in more accurate . This hybrid method is used for learning RBF neural network and uses it for function approximation . Both PSO and TS method use the strategy of best solution of neighborhood in every iteration to get into best result , but every one of them has issues . By use of advantages of this methods we proposed the new method that uses TS to optimize the constants of PSO . numerical result show that the proposed method has improved results contrast to the simple PSO and gradient descent method to learn the RBF neural Network .
[1] Kennedy, J.; Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.

[2] Shi, Y.; Eberhart, R.C. (1998). A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.

[3] F. Glover and C. McMillan . The general employee scheduling problem: an integration of MS and AI. Computers and Operations Research,1986.

[4] J. J. HOPFIELD Neural networks and physical systems with emergent collective computational abilities. Proc. NatL Acad. Sci. USA Vol. 79, pp. 2554-2558, April 1982 Biophysics

[5] R.C. Eberhart and J. Kennedy A New Optimizer using Particle Swarm Theory. In Proceedings of the Sixth International Symposium on Micromachine and Human Science, 1995, pp 39–43.

[6] J. Kennedy. Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance. In Proceedings of the IEEE Congress on Evolutionary Computation, volume 3,July 1999,pages 1931–1938.

[7] J. Kennedy and R. Mendes Population Structure and Particle Performance. In Proceedings of the IEEE Congress on Evolutionary Computation, 2002. pages 1671– 1676. IEEE Press.

[8] J. Kennedy, R.C. Eberhart, and Y. Shi. Swarm Intelligence. Morgan Kaufmann, 2001.

[9] W.-K. Chen, Linear Networks and Systems. Belmont, Calif.: Wadsworth, pp. 123-135, 1993. (Book style)

[10] F.Glover, M.Laguna, Tabu Search, Univ. Boulder, 1997.

[11] A.hertz, E.Taillard, D.De werra, A Tutorial on Tabu Search, ORSpektrum 11, 1989, 131-141.

[12] A.Sebastian and P.Kumar and M.P.Schoen.AStudy on Hybridization of Particle Swarm and Tabu Search Algorithms for unconstrained Optimization and estimation Problems, 14th WSEAS international conference on systems 2010.pp.458-463.

[13] M.Pant, R.Thangaraj, A.Abraham. A New PSO Algorithm with Crossover Operator for Global Optimization Problems. Springer-Verlag Berlin Heidelberg 2007.

[14] N.Alamelumangai, J.D.Shree. PSO Aided Neuro Fuzzy Inference System for Ultrasound Image Segmentation. International Journal of Computer Applications, volume 7-No.14,October 2010.

[15] A.G.Bros. Introduction of the Radial Basis Function (RBF) Networks. Online Symposium of Electronics Engineering. issue 1, vol. 1, DSP Algorithms: Multimedia, Feb. 13 2001, pp. 1-7.

[16] N.Holden,A.A.Frietas. A Hybrid PSO/ACO Algorithm for classification GECCO (Companion) 2007: 2745-2750

[17] M.Vakil-Baghmisheh. N.Pasevic. Training RBF networks with selective back propagation . Elsevier, neurocomputing 62, 2004, pp.39-64.

[18] J.Q.Li, Q.K.Pan, S.X.Xie, B.X.Jia, Y.T.Wang. A hybrid particle swarm optimization and tabu search algorithm for flexible job-shop scheduling problem. International Journal of Computer Theory and Engineering, vol 2, No.2 April, 2010.

[19] Y.Zhang. L.Wu. A Hybrid TS-PSO Optimization Algorithms. Journal of Convergence Information Technology, volume 6, No.5, May 2011.

[20] S.Ferrari, R.Robbert, F.Stengel. Smooth FunctionApproximation Using Neural Network. IEEE transaction on neural network, vol 16, No.1, Jan.2005.

[21] Z.Zainuddin, O.Pauline. Function Approximation Using Artificial Neural Network. WSEAS transaction on Mathematics, issue 6, Vol 7, June 2008.

[22] J.Brownlee. Clever Algorithms: Nature-Inspired Programming Recipes. First Edition, Lulu Enterprises, January 2011. ISBN: 978-1-4467- 8506-5.

[23] S.L.Ho, S.Yang, G.ni, H.C.Wong. A Particle Swarm Optimization Method With Enhanced Global Search Ability for Design Optimizations of Electromagnetic Devices. IEEE transaction on magnetic. Vol. 42, No.4, April 2006.

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