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Reseach Article

Hybrid Heuristic Optimization for Benchmark Datasets

by Pravin Kshirsagar, Sudhir Akojwar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 7
Year of Publication: 2016
Authors: Pravin Kshirsagar, Sudhir Akojwar
10.5120/ijca2016910853

Pravin Kshirsagar, Sudhir Akojwar . Hybrid Heuristic Optimization for Benchmark Datasets. International Journal of Computer Applications. 146, 7 ( Jul 2016), 11-16. DOI=10.5120/ijca2016910853

@article{ 10.5120/ijca2016910853,
author = { Pravin Kshirsagar, Sudhir Akojwar },
title = { Hybrid Heuristic Optimization for Benchmark Datasets },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number7/25409-2016910853/ },
doi = { 10.5120/ijca2016910853 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:45.512566+05:30
%A Pravin Kshirsagar
%A Sudhir Akojwar
%T Hybrid Heuristic Optimization for Benchmark Datasets
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 7
%P 11-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduces hybridization of particle swarm optimization (PSO) with genetic algorithm (GA) denoted as PSO+GA provides an efficient approach which is used to solve non linear chaotic datasets. The proposed algorithm employed in probabilistic neural network(PNN) which is a variant of radial basic function artificial neural network (RBFANN) for finding precise value spread factor for accurate classification of chaotic time series. Hybridizing of particle swarm optimization (PSO) and genetic algorithm (GA) in social learning helps collective efficiency, robustness and global effectiveness. The hybrid approached which then is resulted in the integrated framework for complete determination of spread factor with evaluation parameters. The algorithm is tested on two benchmark problems and compared the performance with arbitrary spread factor of PNN. The results showed that the PSO+GA based heuristic optimization algorithm outperform in terms of higher classification and prediction accuracies with short computation time.

References
  1. Russell Eberhart and James Kennedy, “A New Optimizer Using Particle Swarm Theory”, sixth international symposium on Micro Machine and Human Science, IEEE, volume 8, issue 95, pp. 39-43, 1995.
  2. Kennedy, I., Eberhart, R., Particle Swarm Optimization, Proc. IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, volume 3, issue 95,, pp. 1942- 1948, I995.
  3. Shi, Y., Eberhart, R., “Parameter Selection in Particle Swarm Optimization”, Proceedings of the Seventh Annual Conference on Evolutionary Programming, pp. 591-601, Springer-Verlag, New York, 1998.
  4. R.C. Eberhart and Y. Shi.,” Comparing inertia weights and constriction factors in particle swarm optimization”, Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84-88, 2000.
  5. M. Clerc and J. Kennedy,” The particle swarm - explosion, stability, and convergence in a multidimensional complex space”, IEEE Transactions on Evolutionary Computation, vol. 6, pp. 58-73, 2002.
  6. S. Ujjin and P. J. Bentley, “Particle Swarm Optimization Recommender System”, IEEE Swarm Intelligence Symposium 2003, volume 24, issue 26, pp. 124-131, April 2003.
  7. F. Van den Bergh, “Particle Swarm Weight Initialization in Multi-Layer Perceptron Artificial Neural Networks”, Development and Practice of Artificial Intelligence Techniques, Durban, South Africa, pp. 41-45, 1999.
  8. Zhiyong li, Wei Zhou, Bo Xu, Kenlili, ”An ant colony genetic algorithm based on pheromone diffusion”, fourth IEEE international conference on natural computation, volume 7, pp. 471-474, 2008.
  9. Thomas Stutzle and Holger H. Hoos, “Min-Max Ant System”, Future generation computer systems, Elsevier, pp. 889-914, 2000.
  10. Marco Dorigo and Thomas Stutzle, “Ant colony optimization”, A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England, 2003.
  11. M. Dorigo, V. Maniezzo and A. Colorni, “The Ant System: Optimization by a colony of cooperating agents”, IEEE Transactions on Systems, Man and Cybernetics – Part B, volume 26, issue 1, pp. 29-41, 1996.
  12. B. S. Jung1, B. W. Karney and M. F. Lambert,” Benchmark Tests of Evolutionary Algorithms: Mathematic Evaluation and Application to Water Distribution Systems”, Journal of Environmental Informatics vol. 7, issue 1, pp. 24-35, 2006.
  13. Rahib H. Abiyev and Mustafa Tunay,” Optimization of High-Dimensional Functions through Hypercube Evaluation”, Computational Intelligence and Neuroscience, Hindavi, volume 2015, pp. 1-11.
  14. Lutz Prechelt, Fakultat Fur. 1994. A set of Neural Network Benchmark Problems and Benchmarking Rules. Technical Report 21/94, University of Germany.
  15. Neural Networks reference manual, pdf, www.mathworks.com.
  16. Box, G. E. P., & Jenkins, G. M. "Time Series Analysis, forecasting and control", San Fransisco, CA: Holden Day (1970).
  17. UC Irvine Machine Learning Repository (Center for Machine Learning and Intelligent Systems), http://archive.ics.uci.edu/ml/
  18. Pravin Kshirsagar and Sudhir Akojwar.” Prediction of Neurological Disorders using PSO with GRNN” In the Proceeding of IEEE International Conference on Communication Networks, ICCN.2015.
  19. Pravin Kshirsagar and Sudhir Akojwar.” Novel Approach for Classification and Prediction of Non Linear Chaotic Databases “” In the Proceeding of IEEE-ICEEOT-2016
Index Terms

Computer Science
Information Sciences

Keywords

Particle swarm optimization (PSO) probabilistic neural network (PNN) convergence benchmark genetic algorithm (GA) radial basic function artificial neural network (RBFANN).