Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nations
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.
2019
2021-03-31 23:29:00
1033
ant colony optimization, bat algorithm, common service center, e-governance, fuzzy clustering, meta-heuristic algorithm, particle swarm optimization
Rajan
Gupta
70
Sunil K.
Muttoo
70
Saibal K.
Pal
70
COBISS_ID
3
57967619
UDK
4
004
ISSN pri članku
9
2093-744X
DOI
15
10.5391/IJFIS.2019.19.4.290
NUK URN
18
URN:SI:UNG:REP:LKE8DYU1
0
Predstavitvena datoteka
2021-04-01 08:33:57
ijfis-19-290.pdf
534400
Predstavitvena datoteka
2021-03-31 23:29:12
0
Izvorni URL
2021-03-31 23:29:01