Impact of Number of Artificial Ants in ACO on Network Convergence Time: A Survey

Due to the dynamic nature of computer networks today, there is need to make the networks self-organized. Selforganization can be achieved by applying intelligent systems in the networks to improve convergence time. Bio-inspired algorithms that imitate real ant foraging behaviour of natural ants have been seen to be more successful when applied to computer networks to make the networks self-organized. In this paper, we studied how Ant Colony Optimization (ACO) has been applied in the networks as a bio-inspired algorithm and its challenges. We identified the number of ants as a drawback to guide this research. We retrieved a number of studies carried out on the influence of ant density on optimum deviation, number of iterations and optimization time. We found that even though some researches pointed out that the numbers of ants had no effect on algorithm performance, many others showed that indeed the number of ants which is a parameter to be set on the algorithm significantly affect its performance. To help bridge the gap on whether or not the number of ants were significant, we gave our recommendations based on the results from various studies in the conclusion section of this pape

An Enhanced Bio-Inspired Aco Model For Fault-Tolerant Networks

This research mainly aimed at establishing the current functionality of computer network systems, evaluating the causes of network faults, and developing an enhanced model based on the existing ACO model to help solve these network issues. The new model developed suggests ways of solving packet looping and traffic problems in common networks that use standard switches. The researcher used simulation as a method of carrying out this research whereby an enhanced algorithm was developed and used to monitor and control the flow of packets over the computer network. The researcher used an experimental research design that involved the development of a computer model and collecting data from the model. The traffic of packets was monitored by the Cisco Packet Tracer tool in which a network of four computers was created and used to simulate a real network system. Data collected from the simulated network was analyzed using the ping tool, observation of the movement of packets in the network and message delivery status displayed by the Cisco Packet Tracer. In the experiment, a control was used to show the behavior of the network in ideal conditions without varying any parameters. Here, all the packets sent were completely and correctly received. Secondly, when a loop was introduced in the network it was found that the network was adversely affected because for all packets sent by the computers on the network, none of them was delivered due to stagnation of packets. In the third experiment, still, with the loops on, a new ACO model was introduced in the cisco packet tracer used to simulate the network. In this experiment, all the packets sent were completely and correctly delivered just like in the control experiment