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An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks

Authors:

Shijin WangAuthors Info & Claims

Neural Processing Letters, Volume 26, Issue 3

Pages 217 - 231

https://doi.org/10.1007/s11063-007-9053-x

Published: 01 December 2007 Publication History

Abstract

This paper presents a new evolutionary artificial neural network (ANN) algorithm named IPSONet that is based on an improved particle swarm optimization (PSO). The improved PSO employs parameter automation strategy, velocity resetting, and crossover and mutations to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. IPSONet uses the improved PSO to address the design problem of feedforward ANN. Unlike most previous studies on only using PSO to evolve weights of ANNs, this study puts its emphasis on using the improved PSO to evolve simultaneously structure and weights of ANNs by a specific individual representation and evolutionary scheme. The performance of IPSONet has been evaluated on several benchmarks. The results demonstrate that IPSONet can produce compact ANNs with good generalization ability.

References

[1]

1. Hush DR, Horne NG (1993) Progress in supervised neural networks. IEEE Signal Process Mag 10:8-39.

[2]

2. Angeline PJ, Saunders GM, Pollack JB (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Netw 5(1):54-65.

Digital Library

[3]

3. Maniezzo V (1994) Genetic evolution of the topology and weight distribution of neural networks. IEEE Trans Neural Netw 5(1):39-53.

Digital Library

[4]

4. Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8(3):694-713.

Digital Library

[5]

5. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9): 1423-1447.

[6]

6. Schindler KH, Fischer MM (2000) An incremental algorithm for parallel training of the size and the weights in a feedforward neural network. Neural Process Lett 11:131-138.

[7]

7. Castillo PA, Carpio J, Merelo JJ, Prieto A, Rivas V (2000) Evolving multilayer perceptrons. Neural Process Lett 12:115-127.

Digital Library

[8]

8. Yang JM, Kao CY (2001) A robust evolutionary algorithm for training neural networks. Neural Comput Appl 10::214-230.

[9]

9. Kennedy J, Eberhart RC (1995) Particle swarm optimization. IEEE Int Conf. Neural Networks. Piscataway, pp 1942-1948.

[10]

10. Salerno J (1997) Using the particle swarm optimization technique to train a recurrent neural model. 9th International Conference on Tools With Artificial Intelligence (ICTAI97). IEEE Press, pp 45-49.

[11]

11. Juang CF (2004) A hybrid genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern 32:997-1006.

Digital Library

[12]

12. DaY, Ge XR (2005) An improved PSO-based ANN with simulated annealing techinique. Neurocomput Lett 63:527-533.

Digital Library

[13]

13. Settles M, Rodebaugh B, Soule T (2003) Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network. In: Cantú-Paz E et al. (eds), Genetic and Evolutionary Computation--GECCO-2003. Chicago, 12-16 July, Springer-Verlag. 2723 pp 148-149.

[14]

14. Ratnaweera A, Saman K, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varing acceleration coefficients. IEEE Trans Evol Comput 8(3):240-255.

Digital Library

[15]

15. Angeline PJ (1998) Evolutionary optimization verses particle swarm optimization: philosophy and performance difference. In: Lecture notes Computer Science, vol 1447, Proc. 7th Int. Conf. Evolutionary Programming-Evolutionary Programming VII, Mar. 1998, pp 600-610.

[16]

16. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proc. IEEE Int. Conf. Evolutionary Computation. pp 69-73.

[17]

17. Shi Y, Eberhart RC (1999) Empirical study of particle swarm Opimization. In: Proc. IEEE Int. Conf. Evolutionary Computation 3 pp 101-106.

[18]

18. Lovbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimiser with breeding and subpopulations. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). San Francisco, CA, July 2001.

[19]

19. Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation, in: Proc. of the IEEE Swarm Intelligence Symp. Indianapolis: IEEE Inc pp 72-79.

[20]

20. Murphy PM, Aha DW (1994) UCI repository of machine learning databases. Dept. Inf. Comput. Sci., Univ. California, Irvine, CA.

[21]

21. Prechelt L (1994) Proben 1--A set of benchmarks and benchmarking rules for neural network training algorithms. Univ. Karlsruhe, Karlsruhe, Germany.

[22]

22. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: proceeding of the fourteenth international joint conference on artificial intelligence. Morgan Kaufmann, San Francisco, CA, pp 1137-1143.

[23]

23. Friedman N (1997) Bayesian network classifiers. Mach Learn 29:131-163.

Digital Library

[24]

24. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco.

[25]

25. Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural and statistical classification. Ellis Horwood Limited, London, UK.

[26]

26. Stone M (1974) Cross-validation choice and assessment of statistical predictions. J Royal Stat Soc 36:111-147.

[27]

27. Liu Y, Yao X, Tetsuya HC (2000) Evolutionary ensemble with negative correlation learning. IEEE Transaction on Evolutionary Computation 4(4):380-387.

Digital Library

[28]

28. Abbass HA (2001) A memetic pareto evolutionary approach to artificial neural networks. Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence. Lect Notes Comput Sci 2256:1-12.

Cited By

Deng LLiu S(2023)A Novel Hybrid Grasshopper Optimization Algorithm for Numerical and Engineering Optimization ProblemsNeural Processing Letters10.1007/s11063-023-11230-355:7(9851-9905)Online publication date: 1-Dec-2023
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An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks

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Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 26, Issue 3

December 2007

102 pages

ISSN:1370-4621

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Copyright © Copyright © 2007 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2007

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Cited By

Deng LLiu S(2023)A Novel Hybrid Grasshopper Optimization Algorithm for Numerical and Engineering Optimization ProblemsNeural Processing Letters10.1007/s11063-023-11230-355:7(9851-9905)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11230-3
Bai LLi HGao WXie J(2022)A cooperative genetic algorithm based on extreme learning machine for data classificationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07202-926:17(8585-8601)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s00500-022-07202-9
Telikani ATahmassebi ABanzhaf WGandomi A(2021)Evolutionary Machine Learning: A SurveyACM Computing Surveys10.1145/346747754:8(1-35)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3467477
Eappen GShankar T(2020)A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio NetworkSN Computer Science10.1007/s42979-020-00372-z1:6Online publication date: 20-Oct-2020
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Ghanem WJantan A(2020)A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithmNeural Computing and Applications10.1007/s00521-019-04655-232:15(11665-11698)Online publication date: 1-Aug-2020
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Laxmi Sree BVijaya M(2019)Temperature controlled PSO on optimizing the DBN parameters for phoneme classificationInternational Journal of Speech Technology10.1007/s10772-018-09586-222:1(143-156)Online publication date: 1-Mar-2019
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Garro BVázquez R(2015)Designing Artificial Neural Networks using particle swarm optimization algorithmsComputational Intelligence and Neuroscience10.1155/2015/3692982015(61-61)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/369298
Cui HFeng JGuo JWang T(2015)A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series predictionKnowledge-Based Systems10.1016/j.knosys.2015.07.03288:C(195-209)Online publication date: 1-Nov-2015
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Liao SHsieh JChang JLin C(2015)Training neural networks via simplified hybrid algorithm mixing Nelder---Mead and particle swarm optimization methodsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1292-y19:3(679-689)Online publication date: 1-Mar-2015
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Dehuri SRoy RCho SGhosh A(2012)An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classificationJournal of Systems and Software10.1016/j.jss.2012.01.02585:6(1333-1345)Online publication date: 1-Jun-2012
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