Base Station Localization using Artificial bee colony Algorithm

Wireless communication has observed gigantic advancement since the beginning of this century. The requirement of optimal use of available resources has pushed researchers towards investigation of swarm intelligence based optimization algorithms to support designs and planning decisions. This work, considered how to optimally determine locations of Base Transceiver Station (BTS), such that minimum number of BTS can be installed to cover larger number of subscriber at lesser infrastructural cost. Population based Evolutionary Algorithms (EAs) are developed by modeling the behaviors of different swarms of animals and insects, e.g., ants, termites, bees, birds, fishes. These EAs can be used to obtain near optimal solutions for NP-Hard arbitrary optimization problems. Artificial Bee Colony (ABC) algorithm is a metaheuristic search algorithm and is investigated, in this paper, to localize BTSs so as to cover maximum number of subscribers. The results are then compared with K-Mean clustering method.