Comparative Analysis of Genetic Algorithm and Particle Swam Optimization: An Application in Precision Agriculture

Authors

  • Oluleye Babatunde eAgriculture Research Group, Edith Cowan University, Perth, WA, Australia
  • Leisa Armstrong eAgriculture Research Group, Edith Cowan University, Perth, WA, Australia
  • Jinsong Leng Security Research Institute, Edith Cowan University, Perth, WA, Australia
  • Dean Diepeveen Department of Agriculture and Food, South Perth, 6067, WA, Australia

Keywords:

Genetic Algorithm, Particle Swam Optimization, Feature selection, Precision Agriculture.

Abstract

This article details the exploration and application of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) for the wrapped-based feature selection. Particularly a comparative study is carried out, examining the performances of both GA and PSO with respect to classification accuracy of some classifiers. 112 features were extracted features from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Legendre Moments (LM), Hu's Moments (Hu7M), Texture Properties (TP), Geometrical Properties (GP), and Colour features (CF). The main contribution of this article includes the comparison of two major optimization techniques, i.e., GA and PSO, and the development of a GA-based feature selector using a novel fitness function which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The effectiveness of these manifold projection techniques were tested on Probabilistic Neural Networks (PNN), k Nearest Neighbour (kNN) and Multilayer Perceptron (MLP).  The experimental analysis demonstrates the classification accuracy with GA-based approach outperforming that with PSO-based method.

Author Biographies

Oluleye Babatunde, eAgriculture Research Group, Edith Cowan University, Perth, WA, Australia

PhD Research student and Staff at Graduate Research school, Edith Cowan University, Perth, Australia

Leisa Armstrong, eAgriculture Research Group, Edith Cowan University, Perth, WA, Australia

Senior Lecturer  (PhD)

Jinsong Leng, Security Research Institute, Edith Cowan University, Perth, WA, Australia

Research Fellow, PhD

Dean Diepeveen, Department of Agriculture and Food, South Perth, 6067, WA, Australia

Research Scientist (DAFWA) at Department of Agriculture and Food, Government of Western Australia and Adjunct Professor at Murdoch University, Australia

References

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Published

2015-02-15

How to Cite

Babatunde, O., Armstrong, L., Leng, J., & Diepeveen, D. (2015). Comparative Analysis of Genetic Algorithm and Particle Swam Optimization: An Application in Precision Agriculture. Asian Journal of Computer and Information Systems, 3(1). Retrieved from https://ajouronline.com/index.php/AJCIS/article/view/2210

Issue

Vol. 3 No. 1 (2015): February 2015

Section

Articles

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