Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nationsRajan Gupta
, Sunil K. Muttoo
, Saibal K. Pal
, 2019, original scientific article
Abstract: The 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.
Keywords: ant colony optimization, bat algorithm, common service center, e-governance, fuzzy clustering, meta-heuristic algorithm, particle swarm optimization
Published in RUNG: 01.04.2021; Views: 1255; Downloads: 7
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