Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nationsGupta, Rajan (Avtor)
Muttoo, Sunil K. (Avtor)
Pal, Saibal K. (Avtor)
ant colony optimizationbat algorithmcommon service centere-governancefuzzy clusteringmeta-heuristic algorithmparticle swarm optimizationThe telecenter, popularly known as the rural kiosk or common service center, is an important building block for the improvement of e-governance in developing nations as they help in better citizen engagement. Setting up of these centers at appropriate locations is a challenging task; inappropriate locations can lead to a huge loss to the government and allied stakeholders. This study proposes the use of various meta-heuristic algorithms (particle swarm optimization, bat algorithm, and ant colony optimization) for the improvement of traditional clustering approaches (K-means and fuzzy C-means) used in the facility location allocation problem and maps them for the betterment of telecenter location allocation. A dataset from the Indian region was considered for the purpose of this experiment. The performance of the algorithms when applied to traditional facility location allocation problems such as set-cover, P-median, and the P-center problem was investigated, and it was found that their efficiency improved by 20%–25% over that of existing algorithms.20192021-03-31 23:29:00Neznano6394COBISS_ID: 57967619UDK: 004ISSN pri članku: 2093-744XDOI: 10.5391/IJFIS.2019.19.4.290NUK URN: URN:SI:UNG:REP:LKE8DYU1sl