Utilization of Particle Swarm Optimization (PSO) Use as Clustering Algorithm in MANET

Utilization of Particle Swarm Optimization (PSO) Use as Clustering Algorithm in MANET

International Journal of Computer Science and Engineering
© 2019 by SSRG - IJCSE Journal
Volume 6 Issue 11
Year of Publication : 2019
Authors : Neeraj Kumar Jarouliya, Dr. Nirupama Tiwari
: 10.14445/23488387/IJCSE-V6I11P103

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Citation:
MLA Style:

Neeraj Kumar Jarouliya, Dr. Nirupama Tiwari, "Utilization of Particle Swarm Optimization (PSO) Use as Clustering Algorithm in MANET" SSRG International Journal of Computer Science and Engineering 6.11 (2019): 10-14.

APA Style:

Neeraj Kumar Jarouliya, Dr. Nirupama Tiwari,(2019). Utilization of Particle Swarm Optimization (PSO) Use as Clustering Algorithm in MANET. SSRG International Journal of Computer Science and Engineering 6(11), 10-14.

Abstract:

Mobile ad-hoc networks (MANETs) have been proposed to support dynamic scenarios where no infrastructure exists. Each node in the network acts as a host as well as a router and, forwards traffic to other nodes. MANETs can be set up quickly and at low cost in contrast to infrastructure networks, which may wired or wireless.
In this paper, we propose a Particle Swarm Optimization (PSO) to one clustering algorithm, PSO is initialize with a group of random particles (solutions) and then searches for optima by updating generations. In iterations, each particle has updated by following two "best" values. The first one is the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value called Pbest. When a particle takes part of the population as its topological neighbors, the best value is a local best and it is called Lbest. After finding the two best values, the particle updates its
velocity and positions with mathematical equations. The proposed technique is such that each cluster head handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the MAC protocol.
The individuals (Particles) have divided into groups running in four neighborhood nodes simultaneously, extending the algorithm in a distributed computing manner. Simulation study showed that our approach is efficient and effective, especially when the distribution of mobile nodes is dense.

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Key Words:

PSO, Simulated Annealing, Clustering Algorithm, MANET, NS-2