A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel, Ilyas; Xie, Kun; Chellal, Mouna

doi:10.1002/eng2.12124

Cited by 21 publications

(3 citation statements)

References 62 publications

(105 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model [21]. The presented method's aim is to attain solution with better quality and prevent early convergence of MVO approach.…”

Section: Cmvo Based Hyperparameter Optimizationmentioning

confidence: 99%

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Hilal¹,

Al-Rasheed²,

Alzahrani³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts' knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

show abstract

Section: Cmvo Based Hyperparameter Optimizationmentioning

confidence: 99%

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Hilal¹,

Al-Rasheed²,

Alzahrani³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Here, the classifier weight is trained with proposed ECMVRO for generating optimum solution. ECMVRO enhance deep residual network by integrating CMVO [8] and ROA [7] to choose optimum weights for acquiring effectual training of internal model parameters of classifier. The steps of ECMVRO algorithm is given below.…”

Section: Training Of Deep Residual Networkmentioning

confidence: 99%

hybrid optimization driven deep residual network for attack detection and attack mitigation in wireless sensor networks

Reddy¹,

Laxmi²,

Kumar³

2022

ijhs

View full text Add to dashboard Cite

Wireless sensor network (WSN) comprises small sensor nodes that have the ability of sensing platform, store and send data. In addition, the sensor nodes pose limited resource such as energy, computing power. Clustering is an effective manner for diminishing energy usage utilized by sensor nodes for sending data from source to base station (BS). The multi-path routing is utilized for routing data amongst nodes. However, the WSN are susceptible to several types of security issues which can degrade network performance. WSN are susceptible to various types of attacks, which degraded the performance of entire network. Hence, the primary purpose of this research is to design and develop attack detection and attack mitigation system in WSN for detecting the attackers. This paper devises an energy efficient and optimization aware deep model for attack detection and mitigation in WSN. Here, the first step is the simulation of WSN nodes. The overall procedure of the proposed model includes simulation of WSN, cluster head selection, routing to BS, Sybil attack detection in BS and finally attack mitigation in BS. The first step is WSN simulation and then the selection of energy efficient cluster head is done using Lower Energy Adaptive Clustering Hierarchy (LEACH) protocol.

show abstract

“…In this final stage, MVO algorithm is introduced as a hyperparameter optimizer for BIGRU model [24]. MVO approach is inspired by the concepts that theoretically exist in astronomy.…”

Section: Mvo Based Hyperparameter Optimizationmentioning

confidence: 99%

Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Hamza¹,

Hashim²,

Mohamed³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Internet of Everything (IoE), the recent technological advancement, represents an interconnected network of people, processes, data, and things. In recent times, IoE gained significant attention among entrepreneurs, individuals, and communities owing to its realization of intense values from the connected entities. On the other hand, the massive increase in data generation from IoE applications enables the transmission of big data, from contextaware machines, into useful data. Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems (IDS). In this background, the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System (IMVO-DLIDS) for IoT environment. The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment. The proposed IMVO-DLIDS model follows a three-stage process. At first, data pre-processing is performed to convert the actual data into useful format. In addition, Chaotic Local Search Whale Optimization Algorithm-based Feature Selection (CLSWOA-FS) technique is employed to choose the optimal feature subsets. Finally, MVO algorithm is exploited with Bidirectional Gated Recurrent Unit (BiGRU) model for classification. Here, the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model. The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct 6580 CMC, 2022, vol.73, no.3 measures. An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

show abstract

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Cited by 21 publications

References 62 publications

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

hybrid optimization driven deep residual network for attack detection and attack mitigation in wireless sensor networks

Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Contact Info

Product

Resources

About