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Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage

T.A. Enisuoh1 , O.R. Okonkwo2 , N.N. Mbeledogu3

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-7 , Page no. 26-34, Jul-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i7.2634

Online published on Jul 31, 2021

Copyright © T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu, “Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.26-34, 2021.

MLA Style Citation: T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu "Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage." International Journal of Computer Sciences and Engineering 9.7 (2021): 26-34.

APA Style Citation: T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu, (2021). Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage. International Journal of Computer Sciences and Engineering, 9(7), 26-34.

BibTex Style Citation:
@article{Enisuoh_2021,
author = {T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu},
title = {Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2021},
volume = {9},
Issue = {7},
month = {7},
year = {2021},
issn = {2347-2693},
pages = {26-34},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5360},
doi = {https://doi.org/10.26438/ijcse/v9i7.2634}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i7.2634}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5360
TI - Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage
T2 - International Journal of Computer Sciences and Engineering
AU - T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu
PY - 2021
DA - 2021/07/31
PB - IJCSE, Indore, INDIA
SP - 26-34
IS - 7
VL - 9
SN - 2347-2693
ER -

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Abstract

The need to meet the demands of subscribers of wireless services is imperative to Global System for Mobile (GSM) Communication companies. These demands which evolves round maintenance of good network coverage, reduction in service costs and improved Quality of Service (QoS) depends largely on the nature of cellular network masts hoisting. The existing optimization model however, do not handle hoisting of cellular network masts for small size populated areas effectively as a result of insufficient solution space and thus, suffers from poor network coverage. To overcome this challenge, Greedy Algorithm model was proposed. Object Oriented Analysis and Design Methodology (OOADM) was deployed and the proposed model was developed using Java programming language. A comparative analysis between the developed model and Genetic Algorithm was carried out with an aim of determining the best optimization technique for small size populated areas. The statistical results were significant in all the tests performed. The analysis of the results shows that Greedy Algorithm performed better than Genetic Algorithm in optimizing cellular network masts hoisting for small populated areas. Therefore, with this model, optimization of cellular network mast hoisting for small size populated area will be more accurate and hence, more reliable.

Key-Words / Index Term

Greedy Algorithm (GR) ; Genetic Algorithm (GA) ; Mast; Optimization; Coverage

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