Optimal Cluster Head Selection Using Vortex Search Algorithm with Deep Learning-Based Multipath Routing in MANET

Venkatasubramanian, Suresh

doi:10.1007/978-981-19-5845-8_7

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…To deal with this issue and learn to live with network loss, Venkatasubramanian [19] proposed a Vortex Search method, which used a weighted deep learning model to find the multi-path routing and construct an effective including an energy metric for effective CH selection. Multi-path routing using predicted probability sites and path diversion is offered to improve routing presentation without increasing packet overhead.…”

Section: Related Workmentioning

confidence: 99%

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Krishnan

Sankar

Saradhi³

et al. 2023

Int. j. commun. netw. inf. secur.

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Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes.

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Section: Related Workmentioning

confidence: 99%

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Krishnan

Sankar

Saradhi³

et al. 2023

Int. j. commun. netw. inf. secur.

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“…Whether fine-tuning the hyperparameters of a deep neural network, optimizing supply chain logistics, or finding the most efficient route for delivery services, the need for efficient optimization techniques is abundant. To meet this demand, researchers and practitioners have turned to nature-inspired optimization methods, a class of algorithms that draw inspiration from natural processes to tackle complex optimization problems [ 1 , 2 , 3 , 4 , 5 ]. The diversity and complexity of optimization problems in scientific research and practical applications necessitate innovative solutions.…”

Section: Introductionmentioning

confidence: 99%

Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems

Priyadarshini

2024

Biomimetics

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In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm’s feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO’s wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving.

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“…According to the results they obtained, they stated that they achieved noticeable success in terms of the percentage with the proposed algorithm. Venkatasubramanyan et al [21] used optimization techniques to reduce energy consumption and extend the network lifetime in WSNs. While performing these operations, they proposed a more effective hybrid method by adding the tree seed algorithm (TSA) in order to use the moth search algorithm more effectively.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm

Beşkirli,

Kiran

2023

Biomimetics

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Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of passive and active components, including transistors and operational amplifiers, but also require a power supply. In contrast, passive filters only consist of resistors and capacitors. Therefore, active filters are capable of generating signal gain and possess the benefit of high-input and low-output impedance. In order for active filters to be more functional, the parameters of the resistors and capacitors in the circuit must be at optimum values. Therefore, the active filter is discussed in this study. In this study, the tree seed algorithm (TSA), a plant-based optimization algorithm, is used to optimize the parameters of filters with tenth-order Butterworth and Bessel topology. In order to improve the performance of the TSA for filter parameter optimization, opposition-based learning (OBL) is added to TSA to form an improved TSA (I-TSA). The results obtained are compared with both basic TSA and some algorithms. The experimental results show that the I-TSA method is applicable to this problem by performing a successful prediction process.

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Optimal Cluster Head Selection Using Vortex Search Algorithm with Deep Learning-Based Multipath Routing in MANET

Cited by 3 publications

References 29 publications

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems

Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm

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