On Application of Neural Networks' Modeling for Analytical Comparative Study between Two Optimally Selected Made Decisions by Ant Colony Systems

Hassan M. H. Mustafa^1, and Fadhel Ben Tourkia¹

¹Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha, Kingdom of Saudi Arabia

Cite this paper:
Hassan M. H. Mustafa and Fadhel Ben Tourkia. On Application of Neural Networks' Modeling for Analytical Comparative Study between Two Optimally Selected Made Decisions by Ant Colony Systems. American Journal of Educational Research. 2018; 6(4):308-318. doi: 10.12691/education-6-4-3

Abstract

This piece of research presents a comparative analytical study for two diversified, and challenging issues regarding decisions made by Ant Colony Systems. The presented comparative study considers two swarm intelligent collective decisions, which originated from two diverse, and interactive ACS lifestyles with the environment they are living in. By more details, this article introduces the application of Artificial Neural Networks (ANN^S) modeling considered for analogical comparative analysis and evaluation of two optimal selectivity decisions performed due to two competitive, dynamical, and interactive environmental conditions as follows. Firstly, the decisional issue concerned with optimal selectivity of collective decision made for increasing the efficiency of Ant Colony's foraging process by optimally reaching the best selected food source. However, the second decision The second issue is observed while ant insects are famous for their elaborate nest architecture; less well-known is their skill at moving from one nest site to another. Some, like army ants, move so often that they make no permanent structure, bivouacking instead in simple natural shelters. When an ant was tethered inside an unfamiliar nest site location, and unable to move freely, it is capable to release an alarming pheromone from its mandibular gland that signaled other ants to reject this nest site as to avoid presumable danger. Interestingly, the presented realistic simulation of (ANN^S) behavioral learning paradigms results in the analogy between number of neurons and number of ant mates in ant colony systems. Furthermore, realistic ANN modeling results in the analogy between the intelligent behavioral performance of two ACS versus the performance of two diverse ANN learning paradigms.

Keywords:
Artificial Neural Networks Modeling Swarm Intelligence Tandem Running House Hunting Ants Computing Alarm Pheromone Collective Decision Making Temnothorax

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