Dynamic Network Formation Using Ant Colony Optimization

Military and industry are moving toward every device being network enabled and connected for reliable availability of communication and information. To make this type of system a reality, the devices must be capable of forming a network topology on their own in a dynamic environment to ensure that the correct information reaches a desired location and on-time. This research presents three contributions for solving highly dynamic (i.e. drastic change within the network) Multi-commodity Capacitated Network Design Problems (MCNDPs) resulting in a distributed multi-agent network design algorithm. The first contribution incorporates an Ant Colony Optimization (ACO) algorithm Ant Colony System (ACS) to solve the static MCNDP with weak constraints. Second, a new algorithm is developed and has the capability to dynamically adjust its exploration parameter of the solution space. This enhanced algorithm converges quickly and automatically adjusts to the dynamically changing network environment. Third, a distributed approach is created replacing the previous centralized solver. The distributed algorithm produces comparable results, but more importantly calculates the network topology in less than 20 percent of the computation time

An ACO Algorithm for Effective Cluster Head Selection

This paper presents an effective algorithm for selecting cluster heads in mobile ad hoc networks using ant colony optimization. A cluster in an ad hoc network consists of a cluster head and cluster members which are at one hop away from the cluster head. The cluster head allocates the resources to its cluster members. Clustering in MANET is done to reduce the communication overhead and thereby increase the network performance. A MANET can have many clusters in it. This paper presents an algorithm which is a combination of the four main clustering schemes- the ID based clustering, connectivity based, probability based and the weighted approach. An Ant colony optimization based approach is used to minimize the number of clusters in MANET. This can also be considered as a minimum dominating set problem in graph theory. The algorithm considers various parameters like the number of nodes, the transmission range etc. Experimental results show that the proposed algorithm is an effective methodology for finding out the minimum number of cluster heads.Comment: 7 pages, 5 figures, International Journal of Advances in Information Technology (JAIT); ISSN: 1798-2340; Academy Publishers, Finlan

Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201