Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems

Authors

  • Shafaatunnur Hasan Soft Computing Research Group (SCRG),Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
  • Tan Swee Quo Soft Computing Research Group (SCRG),Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
  • Siti Mariyam Shamsuddin Soft Computing Research Group (SCRG),Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
  • Roselina Sallehuddin Soft Computing Research Group (SCRG),Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

DOI:

https://doi.org/10.32890/jict2012.11.3

Keywords:

Artificial neural network, artificial fish swarm algorithm, classification problems

Abstract

Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. The results are compared to other NIC methods, i.e., Particle Swarm Optimization (PSO) and Differential Evolution (DE), in which AFSA gives better accuracy with feasible performance for all datasets.

 

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Published

30-04-2012

Issue

Vol. 11 (2012): Journal of Information and Communication Technology (JICT) Vol. 11, 2012

Section

Articles

How to Cite

Hasan, S., Quo, T. S., Shamsuddin, S. M., & Sallehuddin, R. (2012). Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems. Journal of Information and Communication Technology, 11, 37-53. https://doi.org/10.32890/jict2012.11.3

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2012.11.3

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