A Simple Ant Colony Optimization Algorithm to Select Cluster Heads in Ad Hoc Networks

Abstract

Forming a clustered network structure has been proposed as a solution to increase network performance, scalability, stability and manageability in an ad hoc network. A good clustering algorithm aims to select cluster heads among available nodes so that a number of specific constraints are satisfied; thus the cluster head selection problem is a multiobjective optimization problem. This paper proposes an algorithm on the basis of ant colony optimization (ACO) to be used to solve this problem. The proposed algorithm is a simple, one hop cluster formation algorithm, to form a clustered structure with the minimum number of clusters. The centralized ACO-based clustering algorithm is evaluated and compared with other clustering algorithms in ad hoc networks in terms of cluster density.

Survey and taxonomy of clustering algorithms in 5G

“…In short, ensuring node connectivity provides long-term connectivity in order to enhance QoS (P.1), spectral efficiency (P.3), energy efficiency (or prolong network lifetime) (P.4), as well as to reduce cost (P.5) and the number of clusters in the network (P.6). This can be performed using particle swarm optimization [197], artificial bees colony [198], and ant colony optimization, that extracts social behaviors of bird flocks, bees, and ant colonies, respectively [199]. In addition, the centralized controller can compute and predict future positions and directions of nodes based on the nodes' historical mobility pattern for achieving long-term connectivity [200].…”