Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm

Kankılıç, Sueda; Karpat, Esin

doi:10.3390/app131810301

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the proliferation of data volume and escalating intricacy of computational tasks, there is a growing need for enhanced computing efficiency. Optimization algorithms must not only address diverse practical complex problems [8], [9], [10], but also strive for superior optimization strategies to minimize computational complexity, expedite convergence, and circumvent local optima. This is crucial for enhancing computational efficiency and successfully accomplishing larger and more intricate computational tasks.…”

Section: Introductionmentioning

confidence: 99%

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

Fang,

Cao

2024

IEEE Access

View full text Add to dashboard Cite

This study introduces a novel metaheuristic algorithm, Leaf in Wind Optimization, inspired by the natural phenomenon of falling leaves in the wind. The proposed method simulates the motion response of leaves in varying intensities of wind by establishing models for light wind-driven blades and strong wind-driven blades. The algorithm incorporates motion modes of linear translation and spiral rotation induced by wind, offering a hybrid search framework suitable for both strategies. This approach enables enhanced exploration and exploitation of the search space. The algorithm's performance was evaluated using three challenging benchmark test sets, CEC 2017, CEC 2019 and CEC 2022, as well as an engineering practical problem. Its effectiveness was assessed through comparison with 10 random optimization algorithms, namely: Tree Seed Algorithm, Multi-Verse Optimizer, Salp Swarm Algorithm, Artificial Ecosystem-based Optimization, Hunger Games Search, Fox Optimizer, Spider Wasp Optimizer, AOBLMOA, Enhanced Snake Optimizer, and IbI Logic Algorithm. In the comprehensive testing conducted, the proposed algorithm consistently outperformed other optimizers in approximately 82% of comparisons. Through examination of convergence curves and statistical data, it is evident that Leaf in Wind Optimization demonstrates superior potential compared to the alternative optimizers under consideration.

show abstract

Section: Introductionmentioning

confidence: 99%

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

Fang,

Cao

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Bald eagle search algorithm: a comprehensive review with its variants and applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

Systems Science & Control Engineering

View full text Add to dashboard Cite

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Maghrabi,

Shabanah,

Althaqafi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model's capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures.

show abstract

Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm

Cited by 3 publications

References 19 publications

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

Bald eagle search algorithm: a comprehensive review with its variants and applications

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Contact Info

Product

Resources

About