An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

Xu, Dongxing; Yin, Jianchuan

doi:10.1109/access.2023.3262600

Cited by 12 publications

(2 citation statements)

References 85 publications

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“…The paper presents a MTBWO algorithm, a type of swarm intelligence optimization algorithm. The algorithm is an extension of the BWOA [15], which includes multi-objective optimization and time-varying iteration arrays. This modification boosts the algorithm's capacity for global search and convergence improvement.…”

Section: Bigru Modelmentioning

confidence: 99%

An MTBWO Algorithm Based on BiGRU Model

Yang,

Sun,

Zhang

2024

Electronics

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To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of the initial time window of the vibration data serve as the health features. Next, the HI of the bearing is constructed by computing the Bray–Curtis distance between the bearing’s degradation characteristics and health features. The cloud monitoring platform constantly tracks the health of the bearing and employs the MTBWO-BiGRU model to anticipate the forthcoming state of health. The platform generates an immediate alert when the HI of the bearing overtakes the alteration rate threshold and foresees the condition of the bearing. We compare the MTBWO-BiGRU model with the bidirectional long short-term memory (BiLSTM) and BiGRU models. The results indicate an accuracy level of 92.57%, which is evidently higher than that obtained when using the other two models. Moreover, the MTBWO-BiGRU model is lighter, demonstrating the practicality of the proposed approach.

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Section: Bigru Modelmentioning

confidence: 99%

An MTBWO Algorithm Based on BiGRU Model

Yang,

Sun,

Zhang

2024

Electronics

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“…Python platform was adapted to implement the EOOA-MACNN-DLSTM-based fake news detection model. Several existing algorithms and techniques like the Black Widow Optimization Algorithm (BWO) [34], Arithmetic Optimization Algorithm (AOA) [35],…”

Section: Experimental Layupmentioning

confidence: 99%

Adaptive Weighted Feature Fusion for Multiscale Atrous Convolution based 1DCNN with Dilated LSTM-aided Fake News Detection using Regional

Vasu,

Kalaivani

2023

Preprint

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The people present in the world rely on social media for gathering news, and it is mainly because of the development of technologies. The approaches employed in natural language processing are still deficient in judgment factors, and these techniques frequently rely upon political or social circumstances. Numerous low-level communities in the area are curious after experiencing the negative effects caused by the spread of false information in different sectors. Low-resource languages are still distracted, considering the fact that these techniques are extensively employed in the English language. This work aims to provide an analysis of regional language fake news and develop a referral system with advanced techniques to identify fake news in Hindi and Tamil. This proposed model includes (a) Regional Language Text Collection, (b) Text pre-processing, (c) Feature Extraction, (d) Weighted Stacked Feature Fusion, and (e) Fake News Detection. The text data is collected from the standard datasets. The collected text data is pre-processed and given into the feature extraction using Bidirectional Encoder Representations from Transformers (BERT), Transformer networks, and seq2seq network for extracting the three sets of language text features. These extracted feature sets are inserted into the weighted stacked feature fusion model, where the three sets of extracted features are integrated with the optimized weights that are acquired through the Enhanced Osprey Optimization Algorithm (EOOA). Here, weighted fused features are accomplished and passed toward the fake news detection phase. Fake news detection is performed with Multi-scale Atrous Convolution-based One-Dimensional Convolutional Neural Network with Dilated Long Short Term Memory (MACNN-DLSTM). The fake news in the regional language is detected finally. The experimental analysis is carried out by comparing with the conventional algorithms and detection techniques to showcase the efficiency of the developed regional language-based fake news detection model.

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Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

Thirupathi,

Ranganathan

2024

Int J Communication

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SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

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An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

Cited by 12 publications

References 85 publications

An MTBWO Algorithm Based on BiGRU Model

An MTBWO Algorithm Based on BiGRU Model

Adaptive Weighted Feature Fusion for Multiscale Atrous Convolution based 1DCNN with Dilated LSTM-aided Fake News Detection using Regional

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

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