Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nations

Rajan Gupta (Author), Sunil K. Muttoo (Author), Saibal K. Pal (Author)

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;

Data

Language:	English
Year of publishing:	2019
Typology:	1.01 - Original Scientific Article
Organization:	UNG - University of Nova Gorica
UDC:	004
COBISS:	57967619
ISSN:	2093-744X
Views:	1255
Downloads:	7
Average score:	0 (0 votes)
Metadata:

Other data

URN:	URN:SI:UNG
Pages:	str. 290-298
Volume:	ǂVol. ǂ19
Issue:	ǂno. ǂ4
Chronology:	2019
DOI:	10.5391/IJFIS.2019.19.4.290
ID:	12717688