Jul 2026· International Research Journal of Multidisciplinary Technovation· pp. 354-369· 0 citations· 22 references
TL;DR
The proposed GWO-EAOMDV framework outperforms both traditional EAOMDV and alternative optimization-based routing protocols and reduces end-to-end latency by 15-22% while simultaneously enhancing PDR by 12-18% and improving energy efficiency.
Abstract
In this paper, a parameter adaption method is suggested for MANET routing efficiency enhancement. It combines the EAOMDV protocol with intelligent Grey Wolf Optimization (GWO). Improving the network's routing patterns through the use of several quality-of-service (QoS) indicators is the main goal. Some of these metrics are the rate of packet delivery (PDR), residual energy, end-to-end delay, and routing overhead. By merging normalized performance measures into a single fitness function, the suggested strategy reframes a weighted single-objective problem as route optimization. In order to automatically optimize routing parameters and choose ideal routes, the suggested system uses GWO, which is modeled after the pack structure and hunting methods used by grey wolves. Better fault tolerance and route stability are achieved by the use of EAOMDV, which guarantees the preservation of multiple loop-free pathways. To avoid optimizing faulty or redundant paths, a method called discrete path validation is used. Many simulations have been run with different node densities and mobility parameters. The proposed GWO-EAOMDV framework outperforms both traditional EAOMDV and alternative optimization-based routing protocols. Reduces end-to-end latency by 15-22% while simultaneously enhancing PDR by 12-18% and improving energy efficiency. The robustness of the proposed model was confirmed by statistical validation employing several simulation runs.
Simulation results indicate that HOA-MEPFL-CLCT-RP outperforms existing models in terms of Packet Delivery Ratio (PDR), energy efficiency, End-to-End Delay (E2D), and routing overhead.
Shaleena H, Sumangala K· International journal of com...· 0 citations
With the rise of the concept of green manufacturing, incorporating energy consumption-related objectives into scheduling problems has become an important research field. Combined with actual production scenarios, this study constructs a mathematical model for the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions. To address the limitation of the traditional Genetic Algorithm (GA) in terms of insufficient local search capability, an Adaptive Genetic Algorithm (AGA) is designed to solve the model. A population initialization method that integrates global and local load minimization is proposed to accelerate the elimination of inferior individuals; the elite retention and roulette wheel selection strategies are combined to prevent the algorithm from falling into local optima. Simulation tests based on standard benchmark instances show that the improved GA can effectively solve the MO-FJGSP, significantly improving both the solution speed and quality. This study provides a novel methodological approach to optimizing production scheduling in green manufacturing environments.
Ming-Yue Li, Lina Wang, Jun Wang et al.· Journal of Engineering, Proj...· 0 citations
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, G. Husnain et al.· Telecommunications Systems· 0 citations
The Traveling Salesman Problem (TSP) is a classic NP-hard combinatorial optimization problem where traditional intelligent algorithms often suffer from slow convergence and premature convergence to local optima. This paper proposes a novel hybrid optimization framework (HGA-PSO-ACO) that integrates the global exploration capability of the Genetic Algorithm (GA), the rapid convergence characteristics of Particle Swarm Optimization (PSO), and the positive feedback mechanism of Ant Colony Optimization (ACO) through a three-stage adaptive coordination model. The key design challenge is to determine when each component should dominate the search and how information should be transferred across stages without causing premature homogenization of the population. To address this issue, the algorithm employs fitness variance (FVD) for real-time population state monitoring and implements dynamic algorithm switching strategies. Experimental validation on a 40-city TSP benchmark demonstrates that the hybrid algorithm achieves a 40% faster convergence speed, a 2.1% lower relative error, and a 58.3% reduction in run-to-run standard deviation compared to individual algorithms, indicating improved solution stability together with favorable parameter sensitivity control.
Xuhang Chen, Zheng-Rui Zhang, Ze-Hong Chen et al.· 2026 3rd World Conference on...· 0 citations
This paper proposes an Intelligent Link Failure Prediction and QoS-Aware Routing framework for Mobile Ad Hoc Networks (MANETs) using the Harris Hawk Optimization (HHO) algorithm to achieve reliable and efficient data transmission under highly dynamic network conditions. The proposed model integrates proactive link failure prediction with HHO-based multi-objective route optimization to select stable, energy-efficient, and QoS-compliant paths. The framework is implemented and evaluated using the SimPy simulation environment under a realistic node mobility and traffic workload generated using Random Waypoint mobility with CBR and VBR traffic patterns, which is widely adopted for MANET performance evaluation. The proposed method is compared with Multi-Agent Deep Learning (MADL), Multi-Agent Deep Reinforcement Learning (MADRL), and the Communication-Aware Hierarchical Routing Framework (CAHRF). Experimental results demonstrate that the HHO-based approach significantly improves network performance by increasing the Packet Delivery Ratio (PDR) by 9.8-15.6%, reducing Link Failure Recovery Time by 21.4-34.7%, extending Network Lifetime by 18.2-27.9%, and decreasing Control Packet Cost by 16.5-25.3% compared to the benchmark methods. These improvements confirm that the proposed HHO-driven intelligent routing framework provides a robust, scalable, and QoS-aware solution for reliable communication in highly dynamic MANET environments.
K. Helini, Malleswari Lakkapogu, Suryateja Kothuru et al.· 2026 7th International Confe...· 0 citations
An in-depth review of energy-efficient routing protocols that have been developed for FANETs and highlights the main research challenges, such as high mobility, dynamic topology, routing overhead, scalability, and security, and discusses future research directions to design more intelligent and energy-aware routing protocols.
Ragvinder Kaur, Amit Sharma· Journal of Intelligent Decis...· 0 citations