Glowworm Swarm Optimization Algorithm Research Articles

EEG Based Emotion Analysis using Reinforced Spatio-Temporal Attentive Graph Neural and Context Net Techniques

EEG-based emotion Classification is used to distinguish and observe the mental state or emotions. Emotion classification using EEG is used for medical, security, and various other purposes. There are several deep learning and machine learning techniques that are used to classify EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and a high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN)and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here the input EEG signals are taken from two benchmark datasets namely as DEAP and K-EmoCon datasets. Then the input EEG signals are pre-processed and the features are extracted using ContextNet with Global principal component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) method. Then the weight parameters of the RSTAGNN are optimized using Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The performance of the proposed method using DEAP dataset attains higher accuracy by 24.05%, 12.64%relatedto existing systems, like DWT-SVM-EEG-EA-DEAP and GCNN-LSTM-EEG-EA-DEAP respectively and the performance of the proposed system using K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like SigRep-EEG-EA-K- EEG Based Emotion Analysis using Reinforced Spatio-Temporal Attentive Graph Neural and Context Net Techniques on Deap and K- EmoCon Article and CAT-EEG-EA-K-EmoCon respectively.

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Improved Glowworm Swarm Optimization for Parkinson’s Disease Prediction Based on Radial Basis Functions Networks

Parkinson’s disease is caused by a disruption in the chemical products that enables the communication between brain cells. The brain’s dopamine cells are responsible for movement control, adaptability, and fluidity. Parkinson’s motor symptoms manifest when 60–80% of these cells are damaged due to insufficient dopamine. Researchers are working to find a way to identify the non-motor symptoms that manifest early detection in the disease to stop the disease’s progression because it is believed that the disease starts many years before the motor symptoms. This research presents Parkinson’s disease diagnosis based on deep learning. Processes for feature selection and classification encompass the suggested diagnosis technique. The proposed model searches for the best subset of characteristics using the Improved Glowworm Swarm Optimization (IGSO) algorithm. Radial Basis Functions Networks (RBFN) classifiers evaluate the chosen features. The suggested model is tested using datasets from Parkinson’s Handwriting samples and Parkinson’s Speech and voice with various sound recordings. With an accuracy of about 95.78%, the suggested algorithm forecasts Parkinson’s disease using the VoicePD dataset more precisely.

Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms

Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm.

Hybrid emperor penguin glowworm swarm optimiser for techno-economical optimisation with demand side management in microgrid using multi-objective function

Recently, microgrids have been engaged with Demand Side Management (DSM), which helps to save more energy. Hence, the existing model suffers based on the slow response, and also it cannot restore the system voltage and frequency. Consequently, the voltage and frequency fluctuations in the microgrid become a challenging task. Thus, this research work aims to develop a new model for the microgrid. Here, a new Hybrid Emperor Penguin Glowworm Swarm Optimisation algorithm is developed with the integration of Emperor Penguin Optimiser (EPO) and Glowworm Swarm Optimisation (GSO). Moreover, the techno-economical model minimises the energy supply cost, Diesel Generator (DG), power loss and voltage variations. It implements the Wind Turbines, DG, optimal capacity of PV and Energy Storage Systems, through techno-economical optimisation for offering renewable energy sources. Moreover, the designed model helps to solve the multi-objective function regarding metrics like initial cost, operation cost, DSM, voltage fluctuations, fuel cost, Electricity Supply Costs and network power losses. The performance of the developed model shows 17.6%, 36.9%, 2.4% and 0.4% better performance than HHO, EPO, GSO and IMP-SPA in terms of voltage fluctuations. Throughout the validation, the offered model shows superior performance when compared with state-of-art models.

Application of clustering cooperative differential privacy in spatial crowdsourcing task allocation

A framework for spatial crowdsourcing task allocation based on centralized differential privacy is proposed for addressing the problem of worker’s location privacy leakage. Firstly, by combining two stages of differential privacy noise addition and clustering matching, a spatial crowdsourcing worker dataset with high differential privacy protection can be obtained; Secondly, the dynamic problem of spatial crowdsourcing task allocation is transformed into a static combinatorial optimization problem by dividing the spatiotemporal units and the “delay matching” strategy; Finally, the improved discrete glowworm swarm optimization algorithm is used to calculate the results of spatial crowdsourcing task allocation. It has been demonstrated that, compared to the direct differential privacy noise-adding assignment method and the discrete glowworm swarm optimization assignment method, the proposed method achieves better task assignment results, with the total travel distance reduced by 12.42% and 3.56%, respectively, and the task assignment success rate increased by 11.75% and 3.34%, respectively.

Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images

Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study.

Optimization of Automatic Well Pattern Deployment in High Water-Cut Oilfield

Abstract In view of the problems such as a plurality of dominant water flow channels formed by flushing the reservoir and inferior development effect in the water injection oilfields, reconstructing the current well pattern and providing well pattern evaluation methods are important ways to enhance oil recovery by improving the injection–production relation and increasing the swept area of water flooding. However, the reservoir engineering methods, the simulation methods, and the artificial intelligence algorithms with few objectives enable comprehensive evaluation of the well pattern. In this article, considering multiple evaluation indexes in oilfield development by the glowworm swarm optimization algorithm and niche technology, automatic well pattern optimization is carried out. The glowworm swarm optimization algorithm has the advantage of efficient global search and simpler algorithm flow, which can speed up the convergence and reduce the parameter adjustment. The niche technology can better maintain the diversity of the solutions and solve the multimodal optimization problems more efficiently, accurately, and reliably. The new method was used to optimize the well pattern of one block in a water-flooding oilfield with high water-cut in a certain oilfield. The optimal well pattern is obtained by multiple iterations to maximize the control degree of the well pattern to the sand body. The results indicate that the injection production correspondence ratio and the reserves control degree of the well pattern to the sand body are improved by 4.48% and 7.94%, respectively.

Energy efficient and multi-hop routing for constrained wireless sensor networks

Wireless sensor network (WSN) technique has advanced in several applications, however, the environment has significant energy and security challenges. Therefore, this research proposes a Multi-Hop, Trivial, as well as Protected Routing Solution for Constrained Wireless Sensor Networks. The Chaotic Particle-swarm Krill Herd (CPKH) method, which includes chaotic theory with accelerated PSO (APSO) as a mutation operator into the krill herd method, is used to first choose the energy-efficient cluster heads. Then, to select the optimal path this research proposed a novel Self-Adaptive Step Glow Worm Swarm Optimization (SASGWS) Algorithm. Following that, the Hamming Residue Technique (HRT) is utilized to counteract the malicious attacks in the WSN. The proposed technology, which could result in increased power efficiency as well as high bandwidth, analyses system status on demand as well as dynamically identifies system hazards.

Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation

Medical Research field has been taken continuous efforts to develop an efficient method for detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D U-Net segmentation using digital infrared (IR) thermal imaging system is proposed in this manuscript for the diagnosis of breast cancer (DBC-4D U-Net-DITI). Initially, the digital infrared thermal images are taken from DMR-IR data set as input, and the imageries are pre-processed to maintain local features and compress the dynamic range of image based upon Altered Phase Preserving Dynamic Range Compression (APPDRC) approach by removing the speckle noise. Then, the image segmentation is carried out with the help of 4D U-Net for obtaining the segmented digital infrared thermal image. The 4D U-Net weight parameters are optimized with Glowworm Swarm Optimization Algorithm (GSOA). The segmented regions of digital infrared thermal images are fed to Binarized Spiking Neural Network (BSNN) for classifying the pathology stage as No spread, Early Stage, Localized, Regional and Distant. The proposed approach is executed in MATLAB. The performance of proposed approach attains better accuracy of 39.01%, 28.34%, and 37.45%, better precision of 17.12%, 24.12% and 32.07% when compared to existing approaches like chaotic salp swarm algorithm (CSSA) based segmentation of thermal images for breast cancer identification (DBC-CSSA-DITI), marine-predators-algorithm based segmentation of thermal images for the diagnosis of breast cancer (DBC-MPA-DITI) and diagnosis of breast cancer based upon CNN using thermal imageries (DBC-CNN-DITI) respectively.

An Energy-Efficient Clustering and Fuzzy-Based Path Selection for Flying Ad-Hoc Networks

Flying Ad-hoc Networks (FANET) allow for an ad-hoc networking among Unmanned Aerial Vehicles (UAV), have recently gained popularity in a variety of military and non-militant applications. The existing work used the Glowworm Swarm Optimization (GSO) algorithm to create a self-organization depending on clustering technique for FANET. Owing to UAV increased mobility, network topology might vary over time, providing route discovery and maintenance is one of the most difficult tasks. And also, the network throughput is still more worsened by the network congestion. To solve this problem, the proposed work designed an energy efficient clustering and fuzzy-based path selection for FANET. In this work, initially, the clustering is performed using the UAV distance. For efficient communication and energy consumption, optimal selection of Cluster Head (CH) is performed by using Adaptive Mutation with Teaching-Learning-Based Optimization (AMTLBO) algorithm. To improve the optimal selection of CH nodes, the best fitness values are calculated. The fitness function depends on Link capacity, remaining energy and neighbor UAV distance. Next to that, nodes begin communications as well as transmit their information to their CH. Improved Fuzzy-based Routing (IFR) is introduced for improving the route discovery process. The goal is to find routes that have a high level of flying autonomy, minimal mobility, and a higher Received Signal Strength Indicator (RSSI). As a result, the energy usage of network is decreased, as well as the cluster’s lifespan is extended. Finally, an adaptive and reliable congestion detection mechanism is introduced to transmit the packets with congestion free path. The experimental result shows that the proposed AMTLBO system attains higher performance compared to the existing system in terms of energy usage, throughput, delay, overhead and packet delivery ratio.

Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.

Artificial Intelligence Machine Learning in Healthcare System for improving Quality of Service

A vital element of widespread patient monitoring is consistency in transmission between the patients and the healthcare professionals not including time and position dependencies. Artificial intelligence (AI) and machine learning (ML) techniques have a vast possibility to proficiently handle the automated function of the mobile nodes distributed in the Mobile ad-hoc network (MANET). ML is a part of AI in that the computer algorithms learn themselves by improving from historical experiences. The main issues in MANET are autonomous operation, maximization of a lifetime, coverage of the network, energy utilization, connectivity issues, quality of service, high bandwidth necessity, communication protocol design, etc. ML is valuable for data aggregation, and it saves the energy of mobile nodes and enhances the network lifetime. In this paper, we propose an Artificial Intelligence Machine Learning Algorithm for improving the Quality of Service in MANET (AIMQ). ML techniques based on artificial neural networks (ANN) algorithm is helpful for data aggregation tasks. This approach formed the clusters by node mobility and connectivity. Glowworm Swarm Optimization (GSO) algorithm is applied in every cluster to choose a proficient Cluster Head (CH). Here, we choose the CH by GSO fitness function based on mobile node degree, node distance, node reliability, and energy. ANN algorithm recognizes and chooses the data aggregator with great energy and more extended stability. It updates the weight of input parameters such as node energy, node degree, packet loss ratio, and node delay to reduce the errors. It minimizes the repetitive CHs selection and member nodes’ re-affiliation in a cluster.

Intelligent Hyperparameter-Tuned Deep Learning-Based Android Malware Detection and Classification Model

Recently, Android applications have been playing a vital part in the everyday life as several services are offered via mobile applications. Due of its market dominance, Android is more at danger from malicious software, and this threat is growing. The exponential growth of malicious Android apps has made it essential to develop cutting-edge methods for identifying them. Despite the prevalence of a number of security-based approaches in the research, feature selection (FS) methods for Android malware detection methods still have to be developed. In this research, researchers provide a method for distinguishing malicious Android apps from legitimate ones by using a intelligent hyperparameter tuned deep learning based malware detection (IHPT-DLMD). Extraction of features and preliminary data processing are the main functions of the IHPT-DLMD method. The proposed IHPT-DLMD technique initially aims to determine the considerable permissions and API calls using the binary coyote optimization algorithm (BCOA)-based FS technique, which aids to remove the unnecessary features. Besides, bidirectional long short-term memory (Bi-LSTM) model is employed for the detection and classification of Android malware. Finally, the glowworm swarm optimization (GSO) algorithm is applied to optimize the hyperparameters of the BiLSTM model to produce effectual outcomes for Android application classification. This IHPT-DLMD method is checked for quality using a benchmark dataset and evaluated in several ways. The test data demonstrated overall higher performance of the IHPT-DLMD methodology in comparison to the most contemporary methods that are currently in use.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

Ensemble Learning With Tournament Selected Glowworm Swarm Optimization Algorithm for Cyberbullying Detection on Social Media

Ensemble Learning With Tournament Selected Glowworm Swarm Optimization Algorithm for Cyberbullying Detection on Social Media

A Novel Resource Scheduler for Resource Allocation and Scheduling in Big Data Using Hybrid Optimization Algorithm at Cloud Environment

Big Medical Data (BMD) is generated by cellular telephones, clinics, academics, suppliers, and organizations. Collecting, finding, analyzing, and managing the big data to make people's lives better, comprehending novel illnesses, and treatments, predicting results at initial phases, and making real-time choices are the actual issues in healthcare systems. Dealing with big medical data in resource scheduling is a major issue that aims to offer higher quality healthcare services. Hadoop MapReduce has been widely used for parallel processing of large data tasks and efficient job scheduling. The number of big data tasks is constantly growing; it is becoming more essential to minimize their energy usage to reduce the environmental effect and operating expenses. Hence to overcome these disadvantages, we propose a novel resource scheduler for big data using a Hybrid 2-GW Optimization Algorithm (H2-GWOA). We employ the Improved GlowWorm Swarm Optimization Algorithm (IGSOA) and Mean GreyWolf Optimization Algorithm (MGWOA) for optimizing the MapReduce framework in heterogeneous big data. The CloudSim platform was used for the simulations. The performance of the proposed scheduler is proved to be better than the conventional methods in terms of metrics like latency, makespan, resource utilization, skewness, and Central Processing Unit (CPU) consumption.

Using Three-Dimensional Logistic Equations and Glowworm Swarm Optimization Algorithm to Generate S-Box

Using Three-Dimensional Logistic Equations and Glowworm Swarm Optimization Algorithm to Generate S-Box

Performance Analysis of Hybridization of [PIO-GSO] Algorithms in Wireless Sensor Networks

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve augmenting network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. In this research, presents a novel Hybridization of Pigeon Inspired with Glowworm Swarm Optimization (HPIGSO) algorithm based clustering innovation in WSN. This innovative HPIGSO algorithm integrates the good characteristics of Pigeon Inspired Optimization (PIO) algorithm and Glowworm Swarm Optimization (GSO) algorithm. The proposed algorithm operates on three major stages namely initialization, cluster head selection and cluster construction. Once the nodes are deployed, the initialization process takes place. Followed by, Base Station (BS) executes the HPIGSO algorithm and selects the cluster heads effectively. Subsequently, nearby nodes joins the cluster head and becomes cluster members, thereby cluster construction takes place. Finally, the cluster members send the data to cluster heads which is then forwarded to the base station via inter-cluster communication. The performance of the proposed HPIGSO method has been evaluated and compared with QOGSO, PIOA-DS, ALO, GOA and FFOA. Finally the proposed HPIGSO algorithm provides prolonged the lifetime of WSN over the existing clustering techniques.