Journal of Computer Sciences and Applications
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Journal of Computer Sciences and Applications. 2015, 3(3), 79-89
DOI: 10.12691/jcsa-3-3-4
Open AccessArticle

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

Hassan M. H. Mustafa1, , 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

Pub. Date: June 26, 2015

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


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.

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

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