On Comparative Analogy between Ant Colony Systems and Neural Networks Considering Behavioral Learning Performance

Hassan M. H. Mustafa^1, , Ayoub Al-Hamadi², Mohamed Abdulrahman³, Shahinaz Mahmoud⁴ and Mohammed O Sarhan⁵

¹Computer Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, KSA.On leave from Banha University, Egypt

²Institute for Information Technology and Communications, Otto-von-Guericke-University Magdeburg, Germany

³Electrical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, KSA

⁴Instructional & Information Technology Educational Technology Department, Women College,Ain Shams University, Cairo, Egypt

⁵Al-Salt College of Human Sciences, Al- Balqa Applied University Hashemite Kingdom of Jordan

Cite this paper:
Hassan M. H. Mustafa, Ayoub Al-Hamadi, Mohamed Abdulrahman, Shahinaz Mahmoud and Mohammed O Sarhan. On Comparative Analogy between Ant Colony Systems and Neural Networks Considering Behavioral Learning Performance. Journal of Computer Sciences and Applications. 2015; 3(3):79-89. doi: 10.12691/jcsa-3-3-4

Abstract

This article addresses an interesting comparative analytical study. The presented study considers two concepts of diverse algorithmic biological behavioral learning approach. Those concepts for computational intelligence are tightly related to neural and non-neural Systems. Respectively, the first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work. Furthermore, a mouse’s trials during its movement inside figure of eight (8) maze, those aiming to reach optimal solution for reconstruction problem. However, second algorithmic intelligent approach conversely originated from observed activities’ results for non-neural Ant Colony System (ACS). Those results have been obtained after reaching optimal solution solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance shown to be similar for both introduced neural and non-neural systems. Considering observed two systems' performances, it has shown both to be in agreement with learning convergence process searching for Least Mean Square (LMS) error algorithm. Accordingly, adopted ANN modeling is realistically relevant tool systematic observations' investigation and performance analysis for both selected computational intelligence (biological behavioral learning) systems.

Keywords:
artificial neural network behavioral learning ant colony system traveling salesman problem and computational biology

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66. available online at: www.iridia.ulb.ac.be/dorigo/aco.html.
[2]	Pavlov, I.P.: Conditional Reflex, An Investigation of The Psychological Activity of the Cerebral Cortex. New York, Oxford University press, (1927).
[3]	Thorndike, E.L.: Animal Intelligence, Darien, Ct. Hafner, (1911).
[4]	Rumelhart, D.E., Mccland, J.I.: eds. Parallel distributed processing: exploration in microstructure of cognition. vol 1 Cambridge, M.A: MIT press (1986).
[5]	Lippman, R.: An introduction to computing with neural nets. In: IEEE-ASSP magazine April pp. 4-20(1987).
[6]	Bonabeau, E., Theraulaz, G,.: Swarm smarts, MajallatAloloom. vol 17, No 5, pp 4-12,and references therein May (2001).
[7]	Garnier, S., Gautrais, J., Theraulaz, G.: The biological principles of Swarm Intelligence. In: Swarm Intelligence Journal Volume 1, Number 1 / pp.3-31, June, (2007).
[8]	Hassan H. and Watany, M.: On Mathematical Analysis of Pavlovian Conditioning Learning Process using Artificial Neural Network Model. In: 10th Mediterranean Electrotechnical Conf., May 29-3, Cyprus(2000).
[9]	Kandel E., Small systems of neurons. In: Scientific American, vol 3, (1979).
[10]	Bliss, T.V.P., Lomo, T.: Lpng lasting potentiation of synaptic transmission in the dentate area of anaesthetized rabbit following stimulation of perforatant path. In: Journal of Physiology, vol 232 (1973).
[11]	Tsien, J, Z.: Building Of a Brainer Mouse, MajallatAloloom, vol 17, May (2001).
[12]	Javitt, D. C., Coyle, J.T.: Decoding Schizophrenia. In: MajallatAloloom, vol 20, (2004).
[13]	Ghonimy M. A., et al.: Learning ability in neural network model. In: Second International Conference On Artificial Intelligence Application, Cairo, Egypt, 400-413, Jan. 22-24, (1994).
[14]	Hassan H., Watany, M.: On comparative evaluation and analogy for pavlovian and throndikian psycho-learning experimental processes using bioinformatics modeling, In AUEJ (2003).
[15]	Hassan, M., H.: Application of Neural Network model for Analysis and Evaluation of Students Individual Differences. In: Proceeding of ICEENG Conference (1998).
[16]	Ghonaimy M.A., Al – Bassiouni, A.M. and Hassan, H.M “Leaning Of Neural Networks Using Noisy Data”. Second International Conference on Artificial Intelligence Applications, Cairo, Egypt, Jan 22-24, 1994. PP. 387-399.
[17]	Hassan, H. M “On Analysis and Evaluation of Non-Properly Prepared Teachers Based on Character Optical Recognition Considering Neural Networks Modeling” Published at Proceedingsof the International Conference on Pattern Recognition and Image Processing ((ICPRIP'15) that will take place on March 16-17, 2015 Abu Dhabi (UAE).
[18]	Fukaya, M., et.al.: Two level Neural Networks: Learning by Interaction with Environment. In: 1st ICNN, San Diego (1987).
[19]	Hassan, H. M.: On mathematical analysis, and evaluation of phonics method for teaching of reading using artificial neural network models. In: SimMod simulation and modeling conference (2005).
[20]	Haykin S.: Neural Networks, Englewood Cliffs, NJ:Prentice-Hallpp 50-60, (1999).
[21]	D.O. Hebb, “The organization of behaviour”, Wiley, New York (1949).
[22]	Rayner, K., Foorman, B., R., Perfetti, C, A., Pesetsky, D., Seiedenberg, M, S.: How Should Reading Be Taught? In: MajallatAloloom, vol 19, number 6/7 4-11(2003).
[23]	Dorigo, M.: Optimization learning and natural algorithms, (in Italian), Ph.D Thesis Dip. Electronico, Politecnico di Milano, (1992).
[24]	Eric Bonabeau, Marco Dorigo and Guy Theraulaz, Swarm Intelligence: fromnatural to artificialsystems, Oxford UniversityPress, 1999.
[25]	Dorigo, C, M., Maniezzo, V.: Distributed optimization by Ant Colonies. In: Proceedings of The First European conference on Artificial Life (ECAL 91), pp 134-142 Elevier, (1991).
[26]	H. M. Hassan. “On principles of biological information processing concerned with learning convergence mechanism in neural and non-neural bio-systems”, Published at CIMCA, 28-30 Nov. 2005.
[27]	Grossberg, S., Levine, D., S.: Neural dynamics of attentionally–modulated Pavolvian conditioning: Blocking, inter-stimulus, interval and secondary reinforcement. In: Applied Optics (1987).
[28]	Gayla S. Keesee “Learning Theories” Available online at http://teachinglearningresources.pbworks.com/w/page/19919565/Learning%20Theories.
[29]	Hampson, S.E. Connectionistic Problem Solving, Computational Aspects of Biological Learning, Berlin, Birkhouser, 1990.
[30]	H.M.Hassan, Ayoub Al-Hamadi, “On Comparative Analogy Between Ant Colony Systems and Neural Networks Considering Behavioral Learning Performance” published at the 4th Indian International Conference on Artificial Intelligence (IICAI-09) held in Tumkur (near Bangalore), India on December 16-18, 2009.
[31]	Hassan, M.H., 2008 “ A Comparative Analogy of Quantified Learning Creativity in Humans Versus Behavioral Learning Performance in Animals: Cats, Dogs, Ants, and Rats.(A Conceptual Overview), published at WSSEC08 conference held on 18-22 August 2008, Derry, Northern Ireland.
[32]	Zhang et al, 1998: Interpreting neuronal population activity by reconstruction.” Journal of Neurophysiology, 79:1017-44.
[33]	Wilson, M. A. and McNaughton, B. L. Dynamics of the hippocampal ensemble code for space. Science, 261: 1055-8.
[34]	Hassan M. H. Mustafa,et.al. “Comparative Analogy of Neural Network Modeling Versus Ant Colony System (Algorithmic and Mathematical Approach)” Published at the proceeding of International Conference on Digital Information Processing, E-Business and Cloud Computing (DIPECC 2013) that held in Dubai, UAE on October 23-25 2013. Available online at: http://sdiwc.net/conferences/2013/dipecc2013/.
[35]	H.M. Hassan, “On Mathematical Modeling of Cooperative E-Learning Performance During Face to Face Tutoring Sessions (Ant Colony System Approach)”published at IEEE EDUCON 2011, on Education Engineering–Learning Environments and Ecosystems in Engineering Education, held on April 4-6, 2011, Amman, Jordan. Available on line at: http://www.google.com.sa/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=9&ved=0CHQQFjAI&url= http%3A%2F%2Feditlib.org%2Fd%2F45687&ei=rsUFU66SAc-c0wWakIHAAQ&usg=AFQjCNFXdog2WcQE_3DE5-8sVp7aaVH4Lw.
[36]	Rechardson T.,Franks N.R. “Teaching in tandem-running ants”, Nature, Jan 2006,12;439(7073):153.
[37]	Eric Bonabeau, and Guy Theraulaz, “SwarmSmarts” MajallatAloloom, May 2001, vol 17, No 5, pp 4-12.
[38]	Eric Bonabeau, Marco Dorigo and Guy Theraulaz, Swarm Intelligence: fromnatural to artificialsystems, Oxford UniversityPress, 1999.
[39]	H.M. Hassan, “Comparative Performance Analysis for Selected Behavioral Learning Systems versus Ant Colony System Performance (Neural Network Approach)”. Published at the International Conference on Machine Intelligence ICMI 2015. Held on Jan 26-27, 2015, in Jeddah, Saudi Arabia.
[40]	H.M. Hassan, et.al. “Comparative Performance Analysis and Evaluation for One Selected Behavioral Learning System versus an Ant Colony Optimization System” Published at the Proceedings of the Second International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA2015), Manila, Philippines, on Feb. 12-14, 2015.