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 (ANNS) 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 (ANNS) 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.
Highlights
The Ant is one type of social insects that have been evolved from wasp-like ancestors in the mid-Cretaceous of the period between 110 and 130 million years ago
The second paradigm considers collective intelligence as a behavior that emerges through the interaction and cooperation of large numbers of lesser intelligent agents. This paradigm composed of two dominant sub-fields 1) Ant Colony Optimization that investigates probabilistic algorithms inspired by the foraging behavior of ants [1,2], and 2) Particle Swarm Optimization that investigates probabilistic algorithms inspired by the flocking and foraging behavior of birds and fish [3]
That is a structure of the Feed Forward Artificial Neural Network (FFANN) model consisted of three layers comprise nine nodes : an "input" layer of four nodes which denoted by (I1, I2, I3, and I4) is connected to a "hidden" layer of three nodes, which is connected to an "output" layer of two nodes that denoted by (O1, and O2)
Summary
The Ant is one type of social insects that have been evolved from wasp-like ancestors in the mid-Cretaceous of the period between 110 and 130 million years ago. The amount of directional information that a colony gathers increases as a function of migration distance, sort of like a self-organizing route planner." the two problems achieve optimality of decisions by modulating the rate of ‘tandem running’, in which ants workers teach each other the route to either a better food source or a new nest site. Both of the suggested problems are autonomously (Self-organizing) perform selective searching considering speed-accuracy trade off for optimum decision to reach either best source or nest site. That formulation considers two diverse learning paradigms models namely: guided with a teacher equivalently as error correction model (supervised), and learning model without teacher's guidance that equivalent to the self-organized (unsupervised) paradigm. [10,11]
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