Title: Comparative Analogy of Neural Network Modeling Versus Ant Colony System (Algorithmic and Mathematical Approach)

Year of Publication: 2013
Page Numbers: 1-7
Authors: Hassan M. H. Mustafa, Ayoub Al-Hamadi, Nada M. Al-Shenawy, Saeed A. Al-Ghamdi, AbdelAziz M. Al-Bassiouni
Conference Name: The International Conference on Digital Information Processing, E-Business and Cloud Computing (DIPECC2013)
- United Arab Emirates


This piece of research addresses an interdisciplinary, challenging and interesting learning issue. More specifically, it deals with analytical and quantitative study comparing two suggested naturally inspired behavioral learning systems. In other words, this study presents an investigational comparison between two diverse realistic models of biological systems. Namely, these systems are associated with learning at mammalian (Pavlovian) and Ant Colony Systems. Introduced investigations have included behavioral responsive functions, for learning process contributed inside brain neural system (number of neurons), as well as Ant Colony Optimization ACO. Additionally, this work revealed an interesting analogy between both suggested systems considering adaptive mathematical learning equations and algorithms. Moreover, analogous results have been introduced for suggested system versus animal learning performance considering spikes (pulsed) neurons approach.