A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification
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Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.
Abstract
Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.
참고문헌 (27)
- K. Tang, "Multi-strategy adaptive particle swarm optimization for numerical optimization", Engineering Applications of Artificial Intelligence, 37, pp.9~19 (2015)
- R. E. Precup, "Performance analysis of torque motor systems with PID controllers tuned by bacterial foraging optimization algorithms", Proceedings of 2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp.141~146 (2014)
- O. D. Orman, "A comparative study on closed set speaker identification using RBF network and modular networks", Proceedings of 14th Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN), pp.291~296 (2000)
- J. Holmström, "Growing Neural Gas, Experiments with GNG, GNG with Utility and Supervised GNG", M.S, pp.2002
- D. H. Kim, "A hybrid genetic algorithm and bacterial foraging approach for global optimization and robust tuning of PID Controller with Disturbance Rejection", Information Sciences, 177(18), pp.3918~3937 (2007)
- Y. T. Amghar, "Contributions of bio-inspired methods for satellite images classification", Proceedings of International Congress on Telecommunication and Application (2012)
- R. Tinos, "Use of the q-Gaussian function in radial basis function networks", Foundations of Computational Intelligence, 5, pp.127~145 (2009)
- R. Kaur, "Bacterial foraging optimization algorithm for evolving artificial neural networks", International Journal of Applied Information Systems (IJAIS), 8(5), pp.16~19 (2015)
- K. M. Passino, "Distributed optimization and control using only a germ of intelligence", Proceedings of the 2000 IEEE International Symposium on Intelligent Control, pp.5~13 (2000)
- K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control", IEEE Control System Magazine, 22(2), pp.52~67 (2002)
- T. Datta, "Improved adaptive bacteria foraging algorithm optimization of antenna array for faster convergence", Progress In Electromagnetics Research C, 1, pp.143~157 (2008)
- S. Narendhar, "A hybrid bacterial foraging algorithm for solving job shop scheduling problems", International Journal of Programming Languages and Applications(IJPLA), 2(4), pp.1~11 (2012)
- S. Das, "Bacterial foraging optimization algorithm theoretical foundations, analysis, and applications", Foundations of Computational Intelligence, 3, pp.23~55 (2009)
- S. Dasgupta, "Adaptive computational chemotaxis in bacterial foraging optimization : an analysis", IEEE Transactions on Evolutionary Computing, 13(4), pp.919~941 (2009)
- S. Das, "On stability of the chemotactic dynamics in bacterial foraging", IEEE Transactions on System, Man and Cybernetics Part A : Systems and Humans, 39(3), pp.670~679 (2009)
- A. Biswas, "Analysis of the reproduction operator in an artificial bacterial foraging system", Applied Mathematics and Computation, 215(9), pp.3343~3355 (2010)
- P. D. Sathya, "Image segmentation using minimum cross entropy and bacterial foraging optimization algorithm", Proceedings of the International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp.500~506 (2011)
- N. Sanyal, "An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation", Expert Systems with Applications, 38(12), pp.15489~15498 (2011)
- G. Mahapatra, "A study of bacterial foraging optimization algorithm and its applications to solve simultaneous equations", International Journal of Computer Applications, 72(5), pp.1~6 (2013)
- N. Rajasekar, "Bacterial foraging algorithm based solar PV parameter estimation", Solar Energy, 97, pp.255~265 (2013)
- S. M. Abd-Elazim, "A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design", Electrical Power and Energy Systems, 46, pp.334~341 (2013)
- W. Kou, "Application of bacterial colony chemotaxis optimization algorithm and RBF neural network in thermal NDT", E for the identification of defect parameters, 2011, pp.35(3), 1483~1491
- K. M. Passino, "Bacterial foraging optimization", International Journal of Swarm Intelligence Research, 1(1), pp.1~16 (2010)
- F. W. Dahlquist, "Studies of bacterial chemotaxis in defined concentration gradients : a model for chemotaxis toward L-serine", Journal of Supramolecular Structure, 4(3), pp.329~342 (1976)
- N. K. Jhankal, "Bacterial foraging optimization algorithm: a derivative free technique", Proceedings of Nirma University International Conference on Engineering (NUiCONE), pp.1~4 (2011)
- J. B. Edward, "An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability", ISA Transaction, 52(5), pp.622~628 (2013)
- M. El-Abd, "Performance assessment of foraging algorithms vs. evolutionary algorithms", Information Science, 182(1), pp.243~263 (2012)