Clustering in Mobile ad hoc Networks with Differential Evolution

Uday K. Chakraborty

doi:10.1109/CEC.2011.5949890

Clustering in Mobile ad hoc Networks with Differential Evolution

Uday K. Chakraborty

University of Missouri-St. Louis

Research output: Contribution to conference › Presentation

Abstract

This paper presents a new, differential-evolution-based method for solving the problem of optimal selection of cluster-heads and cluster-members in mobile ad hoc networks. A novel encoding scheme is used to represent nodes in the network graph, and randomly-generated networks of different sizes are solved. The present method handles problems of much larger sizes than do the best-known methods in the literature. Empirical results show the superiority of this method over state-of-the-art approaches on two counts: quality of the solution and time to find the solution.

Original language	American English
DOIs	https://doi.org/10.1109/CEC.2011.5949890
State	Published - Jun 5 2011
Event	IEEE Congress of Evolutionary Computation - New Orleans Duration: Jun 5 2011 → …

Conference

Conference	IEEE Congress of Evolutionary Computation
Period	6/5/11 → …

Disciplines

Computer Sciences

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10.1109/CEC.2011.5949890License: Unspecified

Cite this

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title = "Clustering in Mobile ad hoc Networks with Differential Evolution",

abstract = " This paper presents a new, differential-evolution-based method for solving the problem of optimal selection of cluster-heads and cluster-members in mobile ad hoc networks. A novel encoding scheme is used to represent nodes in the network graph, and randomly-generated networks of different sizes are solved. The present method handles problems of much larger sizes than do the best-known methods in the literature. Empirical results show the superiority of this method over state-of-the-art approaches on two counts: quality of the solution and time to find the solution.",

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AB - This paper presents a new, differential-evolution-based method for solving the problem of optimal selection of cluster-heads and cluster-members in mobile ad hoc networks. A novel encoding scheme is used to represent nodes in the network graph, and randomly-generated networks of different sizes are solved. The present method handles problems of much larger sizes than do the best-known methods in the literature. Empirical results show the superiority of this method over state-of-the-art approaches on two counts: quality of the solution and time to find the solution.

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Clustering in Mobile ad hoc Networks with Differential Evolution

Abstract

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