References
[1]. Ben Niu, Hong Wang, Lijing Tan, Li Li. (2011). Improved
BFO with Adaptive Chemotaxis Step for Global
Optimization. Hainan:Seventh International Conference
on Computational Intelligence and Security, 76-80.
[2]. Xin Xu, Yan-heng Liu, Ai-min Wang, Gang Wang and
Hui-ling Chen. (2012). A New Adaptive Bacterial Foraging
Optimizer based on Field. Chongqing:EighthInternational
Conference on Natural Computation (ICNC 2012), 986-
990.
[3]. K. M. Bakwad, S.S. Pattnaik, B. S. Sohi, S. Devi, B.K.
Panigrahi, Sanjoy Das and M. R. Lohokare. (2009). Hybrid
Bacterial Foraging with Parameter free PSO. Coimbatore:
World Congress on Nature & Biologically Inspired
Computing, 1077 - 1081.
[4]. D. H. Kim, A. Abraham, and J.H. Cho. (2007). A Hybrid
Genetic Algorithmand Bacterial Foraging Approach for
Global Optimization. Information Sciences, vo.177,
3918–3937.
[5]. EspinalAndr´ es, Sotelo-Figueroa Marco, Soria-
Alcaraz Jorge A, Ornelas Manuel, PugaHector and
CarpioMart´in. (2011). Comparison of PSO and DE for
training neural networks. Puebla: 10thMexican
International Conference on Artificial Intelligence, 83-87.
[6]. Li Ting and Wu Min (2008). An Enhanced Parallel
Backpropagation Learning Algorithm for Multilayer
Perceptrons. Chongqing: 7thWorld Congress on
Intelligent Control and Automation,5287-5291. vo.4,
1942-1948.
[7]. S ingiresu S. Rao. (2009). Engineering Optimization
Theory and Practice, Fourth Edition.USA: John Wiley & Sons,
Inc.
[8]. Abd-Elazim, E. S. (2010). Optimal PID Tuning for
Load,410-415.
[9]. Arthur Asuncion and David Newman (2007). The UCI
Machine Learning Repository Center for Machine Learning
and Intelligent Systems at the University of California, Irvine,
available at: http://archive.ics.uci.edu/ml/.
[10]. MAN Chun-tao and WANG Kun, ZHANG Li-yong.
(2009). A New Training Algorithm for RBF Neural Network
Based on PSO and Simulation Study. Los Angeles, CA: WRI
World Congress on Computer Science and Information
Engineering, vo.4,646-650.
[11]. Andrich B. van Wyk and Andries P. Engelbrecht.
(2010). Overfitting by PSO Trained Feedforward Neural Networks. Barcelona: IEEE Congress on Evolutionary
Computation (CEC), 1-8.
[12]. Jun Liu and Xiaohong Qiu. (2009). A Novel Hybrid PSO-BP Algorithm
for Neural Network Training. Sanya. Hainan: International
Joint Conference on Computational Sciences and
Optimization, 300-303.
[13]. MarcioCarvalho and Teresa B. Ludermir. (2006). An
Analysis of PSO Hybrid Algorithms For Feed-Forward Neural
Networks Training. Ribeirao Preto, Brazil:Ninth Brazilian
Symposium on Neural Networks,6-11.
[14]. Yong GuiHe and Li Bo. (2009). Price Forecasting
Based on PSO Train BP Neural Network. Wuhan: Power and
Energy Engineering Conference,1-4.
[15]. Hanning Chen, Yunlong Zhu and KunyuanHu.
(2008). Self-Adaptation in Bacterial Foraging Optimization
Algorithm, 3rd International Conference on Intelligent
System and Knowledge Engineering3,1026-1031.
[16]. Kennedy, J. a. (1995). Particle swarm optimization.
Perth, WA: Proceedings, IEEE International Conference on
Neural Networks, Vol.4, 1942-1948.
