Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2528189 |
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| author | Masood Ahmad Abdul Hameed Fasee Ullah Atif Khan Hashem Alyami M. Irfan Uddin Abdullah ALharbi |
| author_facet | Masood Ahmad Abdul Hameed Fasee Ullah Atif Khan Hashem Alyami M. Irfan Uddin Abdullah ALharbi |
| author_sort | Masood Ahmad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9b698e9611e4109a3a4412a035f361e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e9b698e9611e4109a3a4412a035f361e2025-02-03T06:43:38ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/25281892528189Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHocMasood Ahmad0Abdul Hameed1Fasee Ullah2Atif Khan3Hashem Alyami4M. Irfan Uddin5Abdullah ALharbi6Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, PakistanDepartment of Computing and Technology, Iqra University, Islamabad, PakistanDepartment of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, ChinaDepartment of Computer Science, Islamia College Peshawar, Peshawar 25120, PakistanDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi ArabiaIn 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.http://dx.doi.org/10.1155/2020/2528189 |
| spellingShingle | Masood Ahmad Abdul Hameed Fasee Ullah Atif Khan Hashem Alyami M. Irfan Uddin Abdullah ALharbi Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc Complexity |
| title | Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc |
| title_full | Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc |
| title_fullStr | Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc |
| title_full_unstemmed | Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc |
| title_short | Cluster Optimization in Mobile Ad Hoc Networks Based on Memetic Algorithm: memeHoc |
| title_sort | cluster optimization in mobile ad hoc networks based on memetic algorithm memehoc |
| url | http://dx.doi.org/10.1155/2020/2528189 |
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