Jul 2026· International Research Journal of Multidisciplinary Technovation· pp. 226-249· 0 citations· 45 references
TL;DR
The proposed Chaos-Simplex Elephant Herding Optimization (CS-EHO) framework gives rise to more stable clusters, reduces the cost of cluster maintenance, lowers the communication overhead and improves the scalability of the network.
Abstract
In Mobile Ad Hoc Networks (MANETs), choosing an efficient cluster-head (CH) is very important to ensure network stability, scalability and communication efficiency. However, existing clustering algorithms are prone to premature convergence, unstable cluster formation, high re-affiliation rates and high control-message overhead under dynamic network conditions. In order to solve these problems, this paper presents a Chaos-Simplex Elephant Herding Optimization (CS-EHO) algorithm for the cluster-head selection in MANETs. The proposed approach employs chaotic population initialization to increase the diversity of search and avoid the local optima, and the simplex-based local search to improve the exploitation capability and the convergence accuracy. The performance of the proposed CS-EHO is compared with GWOCA, EHO, HBA, MPSO, ABC and DGA using four clustering metrics, namely, average number of clusters, cluster lifetime, re-affiliation rate and control-message overhead, for different network sizes, node speeds and transmission ranges. The experimental results show that CS-EHO always outperforms all benchmark algorithms. For a network size of 500 nodes with a transmission range of 100 m, CS-EHO produced only 26 clusters in comparison with 32, 37, 54, 59, 65 and 102 clusters for GWOCA, EHO, HBA, MPSO, ABC and DGA respectively. CS-EHO achieved cluster lifetime of 79% and 80% for transmission ranges of 100 m, 200 m respectively with a low re-affiliation rate of 0.0372 and control overhead of 0.0032 under high mobility (80 km/h). The hybridization of chaotic exploration and simplex based exploitation significantly improves the cluster-head selection ability of EHO. The proposed CS-EHO framework gives rise to more stable clusters, reduces the cost of cluster maintenance, lowers the communication overhead and improves the scalability of the network. Therefore, CS-EHO is an effective and robust clustering solution for highly dynamic MANET scenarios.
Findings prove that SBOA is an effective and scalable clustering platform that can be applied to real-time FANET deployments during disaster recovery, surveillance, and monitoring operations in large regions.
Zaheer Aslam, Taj Rahman, Ghassan Husnain et al.· Telecommunications Systems· 0 citations
The next-generation Intelligent Transportation Systems (ITS) use Vehicular Ad Hoc Networks. This facilitates real-time vehicle-roadside infrastructure communication. However, maintaining stable cluster topologies and guaranteeing consistent Quality of Service is problematic because of VANETs intrinsic high mobility and adaptable topology. In this paper, proposed a Self-Adaptive Marine Predators Algorithm (SAMPA) with an Aquila Optimizer-based Multi-Objective Routing (AOMOR) strategy for Cluster Head Selection (CHS). Instead of demanding all nodes to maintaining links over the entire network, the local interactions among clusters facilitates in lowering the routing complexity. SAMPA, a nature-inspired metaheuristic algorithm, models the foraging strategies of marine predators in their search for prey. It is employed here to select the most suitable CHs by solving a Multi-Objective Optimization (MOO) problem that balances key metrics, including Node Density (ND), Residual Energy (RE), mobility, Link Quality (LQ), and Connectivity Degree (CD). For the routing process, AOMOR employs the hunting strategies of Aquila (eagle) species, which ivolnve searching, swooping, and attacking behaviors, to determine the optimal communication path. Mean Routing Load (MRL), Packet Delivery Ratio (PDR), throughput, End-to-End (E2E) delay, and Control Packet Overhead (CPO) represent several of the objectives, are taken into account by QoS-aware routing. To further enhance adaptability, the Digital Twin technology is integrated in the suggested method, which facilitates real-time analysis of network activities, traffic prediction, and informed decision-making for routing strategies. Proposed structure in improving the efficiency of CHS and improving QoS provisioning in VANETs are demonstrated by evaluating its performance using important metrics such as PDR, Packet Loss Ratio (PLR), E2E delay, throughput, and Average Residual Energy (ARE).
Unknown authors· international journal of eng...· 0 citations
ThGCDTR-RP is proposed, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection that consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO.
Xuan Yang, Jiaqi Yan, Desheng Wang et al.· Journal of King Saud Univers...· 0 citations
Simulation results demonstrate that the proposed CRAHO framework significantly outperforms benchmark routing protocols in maintaining network stability and optimizing data transmission in highly mobile vehicular environments.
Ataollah Sattari, Ali Ghaffari, Abbas Mirzaei· Discover Internet of Things· 0 citations
Mobile Ad-hoc Networks (MANETs) are a useful means for communication in military and emergency situations, as well as for various types of smart sensors and other mobile applications, since they allow mobile nodes to interact with each other without relying on a fixed communication structure. However, fast-moving and unpredictable nodes result in frequent changes to the topology of the network which also result in inconsistent route selections and more energy used to send and receive packets, thus leading to lower overall performance on the network. This paper introduces an adaptive routing scheme based on the Shrike Optimization Algorithm (ShOA) for improving MANET performance by overcoming these issues. The proposed Scheme identifies the most efficient routing paths through the use of the predation and decision-making abilities of Shrike Birds. This Shrike-based routing design reduces the amount of information lost in the form of packet losses and routing overheads, while providing reliable transfers of data by properly balancing the trade-off between exploring and exploiting. Based on extensive simulation results, the proposed ShOA-based MANET exhibits substantially improved performance in the areas of packet delivery ratios, end-to-end delay, throughput, and overall durabilitys compared to both optimization techniques currently used and conventional routing protocols. Therefore,the results support the conclusion that the Shrike Optimization Algorithm offers aviable option for developing reliable and energy-efficient mobile ad - hoc networks for communication
V. Vishu, S. Sivagnanam, L. Suresh et al.· International Conference on...· 0 citations
Mobile Ad hoc Networks (MANETs) have emerged as a flexible
networking paradigm; however, they are associated with several challenges, including low
throughput, poor packet delivery ratio, high energy consumption, increased latency, and
significant routing overhead.
The Enhanced Hybrid Metaheuristic Ant Lion Optimization (EHMALO) method is
suggested as a solution to these limitations. EHMALO applies the RSSI-based Link Expiration
Time (R-LET), Context-Aware Dynamic Weight Adjustment Model (C-DWAM), and Dual
Phase Exploration-Exploitation Technique (D-PET). These techniques are incorporated along
with an enhanced Ant Colony Optimization (ACO) algorithm that uses the foraging techniques
to explore the path, and an improved Lion Optimization Algorithm (LOA) hunting strategy is
chosen to optimize the routing path. The Ad hoc On-demand Multipath Distance Vector
(AOMDV) routing algorithm is used in conjunction with the combined algorithm. To select the
optimal path, an adaptive adjustment parameter for the feedback mechanism is considered after
the iterations. This suggested approach is then used to assess performance metrics such as
packet delivery ratio, throughput, latency, energy efficiency, and routing overhead.
The effectiveness of the proposed EHMALO algorithm is illustrated using Network
Simulator-3 (NS-3) simulations for various network settings. The results are compared to
traditional techniques such as ACO, LOA, Particle Swarm Optimization-Genetic Algorithm
(PSO-GA), and Hybrid Ant Lion Optimization (HALO) with AOMDV techniques. The Quality
of Service (QoS) metrics have improved significantly as per the results
The suggested EHMALO method performs better, surpassing the drawbacks of
conventional bio-inspired methods. When route discovery and route maintenance are
considered, EHMALO maximizes energy efficiency, throughput, and packet delivery ratio
while minimizing the routing overhead ratio and end-to-end delay.
Thus, the proposed EHMALO performs significantly better than ACO with
AOMDV, LOA with AOMDV, PSO-GA, and HALO algorithms. The outcomes show that
EHMALO has the ability to improve MANET performance in dynamic contexts in a
dependable and scalable manner.
Aparna P. More, Rohini S. Kale, M. Rizvi· Recent Advances in Computer...· 0 citations
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