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
N1 - Publisher Copyright:
© 2014 IEEE.
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.
T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Y2 - 6 July 2014 through 11 July 2014
ER -