Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc

Abstract

In mobile ad hoc networks (MANETs), the topology differs very often due to mobile nodes (MNs). The flat network organization has high topology maintenance messages overload. To reduce this message overload in MANET, clustering organizations are recommended. Grouping MANET into MNs has the advantage of controlling congestion and easily repairing the topology. When the MANET size is large, clustered MN partitioning is a multiobjective optimization problem. Several evolutionary algorithms such as genetic algorithms (GAs) are used to divide MANET into clusters. GAs suffer from premature convergence. In this article, a clustering algorithm based on a memetic algorithm (MA) is proposed. MA uses local exploration techniques to reduce the likelihood of early convergence. The local search function in MA is to find the optimal local solution before other evolutionary algorithms. The optimal clusters in MANET can be achieved using MA for dynamic load balancing. In this work, the network is considered a graph G (V, E), where V represents MN and E represent the communication links of the neighboring MNs. The aim of this study is to find the cluster headset (CH) as early as possible when needed. High-quality individuals are selected for the new population in the next generation. New individuals are generated using the crossover mechanism on the chromosome once the two parents have been selected. Data are communicated via CHs between other clusters. The proposed technique is compared with existing techniques such as DGAC, MobHiD, and EMPSO. The proposed technique overcomes the state-of-the-art clustering schemes in terms of cluster counting, reaffiliation rate, cluster life, and overload of control messages.

Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy

“…In the study of Ahmad et al [19], a Memetic Algorithm (MA)-based clustering algorithm is introduced, devised explicitly for partitioning Mobile Ad Hoc Networks (MANETs) into clusters. The MA elucidates local exploration techniques to avert premature convergence and seeks the optimal local solution prior to employing other evolutionary algorithms.…”

Implementation of Cluster Subleader to Balance the Load of a Cluster Head to Achieve Qos in Manets

“…An optimal position for the center node cannot be determined, because, it depends on intuitive interpretation of centrality which is subjected to the destination's route reply messages [8], [9]. To update the keys of the nodes, a central key distributor is required in key management [10].…”

Mutation Based Hybrid Routing Algorithm for Mobile Ad-hoc Networks

“…There have been several standard technologies that support MANETs, like, IEEE 802. 15.4,ultrawideband,IEEE 802.15.3,IEEE 802.11 and Bluetooth [3]. Due the features MANETs have, they are finding popular applications in coal mining networks [4], military, emergency communications [5], as well as, 5G wide area coverage and ultra-dense network scenes [6].…”