# Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Simulated Annealing

### Cite as:

D. Turgut, B. Turgut, R. Elmasri, and Than V. Le. Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Simulated Annealing. In Proceedings of WCNC'03, pp. 1492–1497, March 2003.

### Abstract:

In this paper, we demonstrate how simulated annealing algorithm can be applied to clustering algorithms used in ad hoc networks; specifically our recently proposed \em weighted clustering algorithm (WCA) is optimized by simulated annealing. As the simulated annealing stands to be a powerful stochastic search method, its usage for combinatorial optimization problems was found to be applicable in our problem domain. The problem formulation along with the parameters is mapped to be an individual solution as an input to the simulated annealing algorithm. Input consists of a random set of clusterhead set along with its members and the set of all possible dominant sets chosen from a given network of \em N nodes as obtained from the original WCA. Simulated annealing uses this information to find the best solution defined by computing the objective function and obtaining the best fitness value. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the MAC protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the \em optimized WCA than the original WCA.

### BibTeX:

@inproceedings{Turgut-2003-WCNC,
author = "D. Turgut and B. Turgut and R. Elmasri and Than V. Le",
title = "Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Simulated Annealing",
booktitle = "Proceedings of WCNC'03",
location = "New Orleans, Louisiana",
month = "March",
year = "2003",
pages = "1492-1497",
abstract = {In this paper, we demonstrate how simulated annealing algorithm
can be applied to clustering algorithms used in ad hoc networks;
specifically our recently proposed {\em weighted clustering algorithm} (WCA)
is optimized by simulated annealing. As the simulated annealing stands to be
a powerful stochastic search method, its usage for combinatorial
optimization problems was found to be applicable in our problem domain. The
problem formulation along with the parameters is mapped to be an individual
solution as an input to the simulated annealing algorithm. Input consists of
a random set of clusterhead set along with its members and the set of all
possible dominant sets chosen from a given network of {\em N} nodes as
obtained from the original WCA. Simulated annealing uses this information to
find the best solution defined by computing the objective function and
obtaining the best fitness value. The proposed technique is such that each
clusterhead handles the maximum possible number of mobile nodes in its
cluster in order to facilitate the optimal operation of the MAC protocol.
Consequently, it results in the minimum number of clusters and hence
clusterheads. Simulation results exhibit improved performance of the {\em
optimized} WCA than the original WCA.},
}


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