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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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Performance Comparison of Hybrid Electromagnetism-Like Mechanism Algorithms with Descent Method

Hirofumi Miyajima
  • Kagoshima University, 1-21-40 Korimoto Kagoshima, Japan
/ Noritaka Shigei
  • Kagoshima University, 1-21-40 Korimoto Kagoshima, Japan
/ Hiromi Miyajima
  • Kagoshima University, 1-21-40 Korimoto Kagoshima, Japan
Published Online: 2015-10-29 | DOI: https://doi.org/10.1515/jaiscr-2015-0035

Abstract

Electromagnetism-like Mechanism (EM) method is known as one of metaheuristics. The basic idea is one that a set of parameters is regarded as charged particles and the strength of particles is corresponding to the value of the objective function for the optimization problem. Starting from any set of initial assignment of parameters, the parameters converge to a value including the optimal or semi-optimal parameter based on EM method. One of its drawbacks is that it takes too much time to the convergence of the parameters like other meta-heuristics. In this paper, we introduce hybrid methods combining EM and the descent method such as BP, k-means and FIS and show the performance comparison among some hybrid methods. As a result, it is shown that the hybrid EM method is superior in learning speed and accuracy to the conventional methods.

References

  • [1] I. Boussaid, J. Lepagnot, P. Siarry: A Survey on Optimization Metaheuristics, Information & Sciences, 237, pp.82-117, (2013) [Web of Science]

  • [2] J. Matyas: Random Optimization, Automation & Remote Contr., 26, pp.246-253 (1965)

  • [3] J. Kennedy, R. Eberhart: Particle Swarm Optimization, IEEE Inf. Conf. on Neural Netwarks, 4, 1942-1948, (1995)

  • [4] N. Baba: A new Approach for Finding the Global Minimum of Error Function of Neural Networks, Neural Networks, 2, pp. 367-373, (1989) [Crossref]

  • [5] S. I. Birbil, S. C. Fang: An Electromagnetismlike Mechanism for Global Optimization, Journal of Global Optimization, 25, pp.263-282, (2003)

  • [6] J. Jiang, H. Shang, K. Liu, Q. Su, L. Zhang: A Clustering Method Using Electromagnetism-like Mechanism Algorithm, Journal of Computational Information Systems, 9, 10, pp.3985-3191, (2013)

  • [7] M. Clerc, J. Kennedy: The Particle Swarm- Explosion, Stability, and Convergence in a Multidimensional Complex Space, IEEE Trans. on Evolutionary Computation, 6, 1, (2002)

  • [8] N. Baba: A Hybrid Algorithm for Finding the Global Minimum of Error Function of Neural Networks and its applications, Neural Networks, 7, 8, pp.1253-1265, (1994) [Crossref]

  • [9] H. Yuan, J. Ahi, J. Liu: Application of Particle Swarm Optimization Algorithms based Fuzzy BP Neural Network for Target Damage Accesment, Scientific Research and Essays, 6, 15, pp.3109-3121, (2011)

  • [10] J. L. Lin, C.H. Wa, H.Y. Chung: Performance Comparison of Electromagnetism-like Algorithms for Global Optimization, Applied Mathematics, 3, pp.1265-1275, (2012) [Crossref]

  • [11] C.H.Lee, C.T.Li, F.Y.Chang: A Species-based improved Electromagnetism-like Mechanism Algorithm for TSK-type integral-valued Neural Fuzzy System Optimization, Fuzzy Set and Systems, 171, pp. 22-43, (2011)

  • [12] M. M. Gupta, L. Jin, N. Honma: Static and Dynamic Neural Networks, IEEE Pres, Wiley- Interscience, (2003)

  • [13] T. M. Martinetz, S. G. Berkovich, K. J. Schulten: Neural Gas Network for Vector Quantization and its Application to Time-series Prediction, IEEE Trans. Neural Network, 4, 4, pp.558-569, (1993)

  • [14] UCI Repository of Machine Learning Databases and Domain Theories, ftp://ftp.ics.uci.edu/pub/machinelearning-Databases

  • [15] C.H. Lee, F.Y. Chang, C.T. Lee: A hybrid of electromagnetism-like mechanism and backpropagation algorithms for recurrent neural fuzzy systems design, Journal of Systems Science, 43, 2, pp. 231-247, (2012) [Web of Science]

  • [16] H.H Chang, T.Y. Huang: Mixture Experiment Design Using Artificial Neural Networks and Electromagnetism-like Mechanism Algorithm, in Proc. Second International Conference on Innovative Computing, Information and Control, pp. 397-397, (2007)

  • [17] X.J. Wang, L. Gao, C.Y. Zhang: Electromagnetism-Like Mechanism Based Algorithm for Neural Network Training, LNAI, 5227, pp. 40-45, (2008)

  • [18] C.H. Lee, F.K. Chang, Y.C. Lee: Nonlinear systems design by a novel fuzzy neural system via hybridization of electromagnetism-like mechanism and particle swarm optimization algorithms, Information Sciences, 186, 1, pp. 59-72, (2012) [Web of Science]

  • [19] S. Fukumoto, H. Miyajima: Learning Algorithms with Regularization Criteria for Fuzzy Reasoning Model, Journal of Innovative Computing Information and Control, 1,1, pp. 249-163, (2006)

About the article

Published Online: 2015-10-29

Published in Print: 2015-10-01



Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0035. Export Citation

© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

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