Static drone placement by elephant herding optimization algorithm

Ivana Strumberger; Nebojsa Bacanin; Slavisa Tomic; Marko Beko; Milan Tuba

doi:10.1109/TELFOR.2017.8249469

Static drone placement by elephant herding optimization algorithm

Ivana Strumberger, Nebojsa Bacanin, Slavisa Tomic, Marko Beko, Milan Tuba

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.

Original language	English
Title of host publication	2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-4
Number of pages	4
Volume	2017-January
ISBN (Electronic)	9781538630723
ISBN (Print)	978-1-5386-3073-0
DOIs	https://doi.org/10.1109/TELFOR.2017.8249469
Publication status	Published - 5 Jan 2018
Event	25th Telecommunications Forum, TELFOR 2017 - Belgrade, Serbia Duration: 21 Nov 2017 → 22 Nov 2017

Conference

Conference	25th Telecommunications Forum, TELFOR 2017
Country/Territory	Serbia
City	Belgrade
Period	21/11/17 → 22/11/17

Keywords

Elephant herding optimization algorithm
metaheuristic optimization
static drone location problem
swarm intelligence

Access to Document

10.1109/TELFOR.2017.8249469

Cite this

@inproceedings{b6379c204ab14ebb9fc67528904578c7,

title = "Static drone placement by elephant herding optimization algorithm",

abstract = "Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.",

keywords = "Elephant herding optimization algorithm, metaheuristic optimization, static drone location problem, swarm intelligence",

author = "Ivana Strumberger and Nebojsa Bacanin and Slavisa Tomic and Marko Beko and Milan Tuba",

note = "sem pdf conforme despacho. Ministry of Education, Science and Technological Development of Republic of Serbia - III-44006 ; 25th Telecommunications Forum, TELFOR 2017 ; Conference date: 21-11-2017 Through 22-11-2017",

year = "2018",

month = jan,

day = "5",

doi = "10.1109/TELFOR.2017.8249469",

language = "English",

isbn = "978-1-5386-3073-0",

volume = "2017-January",

pages = "1--4",

booktitle = "2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

Strumberger, I, Bacanin, N, Tomic, S, Beko, M & Tuba, M 2018, Static drone placement by elephant herding optimization algorithm. in 2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings. vol. 2017-January, Institute of Electrical and Electronics Engineers (IEEE), pp. 1-4, 25th Telecommunications Forum, TELFOR 2017, Belgrade, Serbia, 21/11/17. https://doi.org/10.1109/TELFOR.2017.8249469

Static drone placement by elephant herding optimization algorithm. / Strumberger, Ivana; Bacanin, Nebojsa; Tomic, Slavisa et al.
2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings. Vol. 2017-January Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Static drone placement by elephant herding optimization algorithm

AU - Strumberger, Ivana

AU - Bacanin, Nebojsa

AU - Tomic, Slavisa

AU - Beko, Marko

AU - Tuba, Milan

N1 - sem pdf conforme despacho. Ministry of Education, Science and Technological Development of Republic of Serbia - III-44006

PY - 2018/1/5

Y1 - 2018/1/5

N2 - Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.

AB - Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.

KW - Elephant herding optimization algorithm

KW - metaheuristic optimization

KW - static drone location problem

KW - swarm intelligence

UR - http://www.scopus.com/inward/record.url?scp=85045851065&partnerID=8YFLogxK

U2 - 10.1109/TELFOR.2017.8249469

DO - 10.1109/TELFOR.2017.8249469

M3 - Conference contribution

SN - 978-1-5386-3073-0

VL - 2017-January

SP - 1

EP - 4

BT - 2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 25th Telecommunications Forum, TELFOR 2017

Y2 - 21 November 2017 through 22 November 2017

ER -

Static drone placement by elephant herding optimization algorithm

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this