Particle swarm optimization for mobile ad hoc networks clustering

Abstract

A mobile ad hoc network is an infrastructureless wireless network that can support highly dynamic mobile nodes. The multi-hop feature of an ad hoc network suggests the use of clustering to simplify routing and management. In this work, we propose a revised Particle Swarm Optimization (PSO) to one clustering algorithm, Weighted Clustering Algorithm (WCA), in ad hoc networks. First, we revise the WCA to be suitable for densely populated areas. Then, Divided Range Particle Swarm Optimization (DRPSO) algorithm was applied to this revised WCA optimization. The individuals (particles) were divided in to 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.