Honey bee algorithm–based efficient cluster formation and optimization scheme in mobile ad hoc networks

Ahmad, Masood; Ikram, Ataul Aziz; Lela, Rabo; Wahid, Ishtiaq; Ulla, Riaz

doi:10.1177/1550147717716815

Cited by 22 publications

(7 citation statements)

References 25 publications

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“…The bee algorithm emulates honey bee foraging activities to find the optimal solution to an optimization issue. The food source or flower is considered a candidate solution, and the population of n bees searches the solution space [18]. Two important parameters of a deep learning network are the number of hidden layers and the number of neurons in each layer.…”

Section: Literature Surveymentioning

confidence: 99%

Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques

Korade¹,

Zuber²

2022

IJACSA

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Forecasting the correct stock price is intriguing and difficult for investors due to its irregular, inherent dynamics, and tricky nature. Convolutional neural networks (CNN) have impressive performance in forecasting stock prices. One of the most crucial tasks when training a CNN on a stock dataset is identifying the optimal hyperparameter that increases accuracy. In this research, we propose the use of the Firefly algorithm to optimize CNN hyperparameters. The hyperparameters for CNN were tuned with the help of Random Search (RS), Particle Swarm Optimization (PSO), and Firefly (FF) algorithms on different epochs, and CNN is trained on selected hyperparameters. Different evaluation metrics are calculated for training and testing datasets. The experimental finding demonstrates that the FF method finds the ideal parameter with a minimal number of fireflies and epochs. The objective function of the optimization technique is to reduce MSE. The PSO method delivers good results with increasing particle counts, while the FF method gives good results with fewer fireflies. In comparison with PSO, the MSE of the FF approach converges with increasing epoch.

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Section: Literature Surveymentioning

confidence: 99%

Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques

Korade¹,

Zuber²

2022

IJACSA

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“…The genetic algorithm selected the best cluster head based on the fitness function which was developed using the distance and energy metrics. Ahmad et al, 23 have suggested a honey bee algorithm based cluster head selection for MANET. Metrics, such as mobility direction, remaining energy, node degree, and mobility speed were considered for the cluster head selection.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient and delay aware clustering in mobile adhoc network: A hybrid fruit fly optimization algorithm and whale optimization algorithm approach

Saminathan¹,

Renukadevi²

2022

Concurrency and Computation

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The energy efficient and delay are the two important optimization issues in the mobile adhoc network (MANET), where the nodes move randomly at any direction with limited battery life, resulting in occasional change of network topology. In this article, a hybrid fruit fly optimization algorithm and whale optimization algorithm (FOA-WOA) is proposed for energy efficient with delay aware cluster head (CH) selection. The major objective of the proposed method is "to solve the problems of energy efficient with delay and develop a clustering mechanism". The performance of the hybrid FOA-WOA is evaluated based on packet delivery ratio (PDR), delay, energy consumption, and throughput. Moreover, the proposed method is analyzed with two existing algorithms, like ant colony optimization (ACO) and genetic algorithm (GA). The experimental results show that the proposed method attains 11.6% better than ACO and 1.8% better than GA based on packet delivery ratio, 57.6% better than ACO and 27.3% better than GA based on delay and 15.3% better than ACO and 36.4% better than GA based on energy consumption.

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“…is will improve farming techniques, and production cost (labor) may decrease. e MANET has widely been used for the purpose [24]. e technical nature of wireless sensor nodes makes it difficult to transmit the sensing data in a timely manner without delay.…”

Section: Hybrid Clusteringmentioning

confidence: 99%

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

et al. 2020

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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.

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Honey bee algorithm–based efficient cluster formation and optimization scheme in mobile ad hoc networks

Cited by 22 publications

References 25 publications

Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques

Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques

Energy efficient and delay aware clustering in mobile adhoc network: A hybrid fruit fly optimization algorithm and whale optimization algorithm approach

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

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