An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks

Chung Wei Wu, Tsung Che Chiang, Li Chen Fu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Due to the proliferation of smart mobile devices and the developments in wireless communication, mobile ad hoc networks (MANETs) are gaining more and more attention in recent years. Routing in MANETs is a challenge, especially when the network contains a large number of nodes. The clustering technique is a popular method to organize the nodes in MANETs. It divides the network into several clusters and assigns a cluster head to each cluster for intra- and inter-cluster communication. Clustering is NP-hard and needs to consider multiple objectives. In this paper we propose a Pareto-based ant colony optimization (ACO) algorithm to deal with this multiobjective optimization problem. A new encoding scheme is proposed to reduce the size of search space, and a new decoding scheme is proposed to generate high-quality solutions effectively. Experimental results show that our approach is better than several benchmark approaches.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2963-2968
Number of pages6
ISBN (Electronic)9781479914883
DOIs
Publication statusPublished - 2014 Sep 16
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 2014 Jul 62014 Jul 11

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Other

Other2014 IEEE Congress on Evolutionary Computation, CEC 2014
CountryChina
CityBeijing
Period14/7/614/7/11

Fingerprint

Ant colony optimization
Mobile ad hoc networks
Mobile Ad Hoc Networks
Optimization Algorithm
Clustering
Communication
Multiobjective optimization
Mobile devices
Decoding
Multiple Objectives
Multiobjective Optimization Problems
Vertex of a graph
Proliferation
Pareto
Wireless Communication
Mobile Devices
Search Space
Divides
Assign
Encoding

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Wu, C. W., Chiang, T. C., & Fu, L. C. (2014). An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 2963-2968). [6900458] (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900458

An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks. / Wu, Chung Wei; Chiang, Tsung Che; Fu, Li Chen.

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2963-2968 6900458 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, CW, Chiang, TC & Fu, LC 2014, An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900458, Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 2963-2968, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 14/7/6. https://doi.org/10.1109/CEC.2014.6900458
Wu CW, Chiang TC, Fu LC. An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 2963-2968. 6900458. (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014). https://doi.org/10.1109/CEC.2014.6900458
Wu, Chung Wei ; Chiang, Tsung Che ; Fu, Li Chen. / An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 2963-2968 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).
@inproceedings{9420044e84ca4bd99c3c0c3f3a14655e,
title = "An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks",
abstract = "Due to the proliferation of smart mobile devices and the developments in wireless communication, mobile ad hoc networks (MANETs) are gaining more and more attention in recent years. Routing in MANETs is a challenge, especially when the network contains a large number of nodes. The clustering technique is a popular method to organize the nodes in MANETs. It divides the network into several clusters and assigns a cluster head to each cluster for intra- and inter-cluster communication. Clustering is NP-hard and needs to consider multiple objectives. In this paper we propose a Pareto-based ant colony optimization (ACO) algorithm to deal with this multiobjective optimization problem. A new encoding scheme is proposed to reduce the size of search space, and a new decoding scheme is proposed to generate high-quality solutions effectively. Experimental results show that our approach is better than several benchmark approaches.",
author = "Wu, {Chung Wei} and Chiang, {Tsung Che} and Fu, {Li Chen}",
year = "2014",
month = "9",
day = "16",
doi = "10.1109/CEC.2014.6900458",
language = "English",
series = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2963--2968",
booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

}

TY - GEN

T1 - An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks

AU - Wu, Chung Wei

AU - Chiang, Tsung Che

AU - Fu, Li Chen

PY - 2014/9/16

Y1 - 2014/9/16

N2 - Due to the proliferation of smart mobile devices and the developments in wireless communication, mobile ad hoc networks (MANETs) are gaining more and more attention in recent years. Routing in MANETs is a challenge, especially when the network contains a large number of nodes. The clustering technique is a popular method to organize the nodes in MANETs. It divides the network into several clusters and assigns a cluster head to each cluster for intra- and inter-cluster communication. Clustering is NP-hard and needs to consider multiple objectives. In this paper we propose a Pareto-based ant colony optimization (ACO) algorithm to deal with this multiobjective optimization problem. A new encoding scheme is proposed to reduce the size of search space, and a new decoding scheme is proposed to generate high-quality solutions effectively. Experimental results show that our approach is better than several benchmark approaches.

AB - Due to the proliferation of smart mobile devices and the developments in wireless communication, mobile ad hoc networks (MANETs) are gaining more and more attention in recent years. Routing in MANETs is a challenge, especially when the network contains a large number of nodes. The clustering technique is a popular method to organize the nodes in MANETs. It divides the network into several clusters and assigns a cluster head to each cluster for intra- and inter-cluster communication. Clustering is NP-hard and needs to consider multiple objectives. In this paper we propose a Pareto-based ant colony optimization (ACO) algorithm to deal with this multiobjective optimization problem. A new encoding scheme is proposed to reduce the size of search space, and a new decoding scheme is proposed to generate high-quality solutions effectively. Experimental results show that our approach is better than several benchmark approaches.

UR - http://www.scopus.com/inward/record.url?scp=84908568827&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908568827&partnerID=8YFLogxK

U2 - 10.1109/CEC.2014.6900458

DO - 10.1109/CEC.2014.6900458

M3 - Conference contribution

AN - SCOPUS:84908568827

T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

SP - 2963

EP - 2968

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -