Glowworm Swarm Optimization Algorithm Research Articles

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.

Intelligent deep learning based bidirectional long short term memory model for automated reply of e-mail client prototype

E-mail is considered the commonly used and efficient way of communication over the globe. In the corporate sectors, the number of E-mails received every day is considerably high and the timely response to every E-mail is essential. Several researchers believe that natural language processing (NLP) techniques by the use of deep learning (DL) architectures have played a considerable part to reduce manual work for repeated E-mail responses and intended to develop E-mail systems with intelligent response function. In this view, this paper designs an intelligent DL enabled optimal bidirectional long short term memory (Bi-LSTM) technique for an automated E-mail reply (OBiLSTM-AER) of E-mail Client Prototype. The goal of the proposed model is to provide an automated E-mail reply solution for persons as well as corporates which receive massive identical E-mails daily. The presented model employs Glove and OBiLSTM model for feature extraction of receiving and response E-mails respectively. Finally, softmax classifier is applied to allocate the class labels. For improving the performance of the BiLSTM model, the hyperparameter tuning process takes place using an oppositional glowworm swarm optimization (OGSO) algorithm. An extensive set of simulations were performed to highlight the betterment of the proposed method and the results are examined interms of distinct measures.

Return Rate Prediction in Blockchain Financial Products Using Deep Learning

Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.

Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network

Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understanding the real-time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on Curvelet transform to establish theory model of twin Curvelet support vector machine. In order to improve the prediction precision of the twin Curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

Multi-performance optimization of multi-roller burnishing process in sustainable lubrication condition

ABSTRACT Sustainable machining processes are efficiently achieved using the selection of optimal parameters. In this study, the minimum quantity lubrication-assisted multi-roller burnishing (MQLAMRB) operation is proposed and optimized to reduce the total energy consumption (TE), mean roughness depth (MR), and roundness deviation (RN). Burnishing parameters are the burnishing speed (BS), depth of penetration (DOP), the quantity consumed of the lubricant (QO), and the pressure value of the compressed air (PA). The embodied energy of the lubricant (Eel) and burnishing tool (Eeb) are developed and integrated into the TE model. The artificial neural network (ANN) model of the energy consumption in the burnishing time (Ebo), MR, and RN is proposed regarding the MQLAMRB parameters. The best-selected solution is determined using an efficient glowworm swarm optimization (GSO) algorithm and the TOPSIS. The results indicated that the 4–25-21-25-3 ANN structure effectively used to construct the MQLAMRB performances. The optimal outcomes of the BS, DOP, QO, and PA are 94 m/min, 0.12 mm, 130 ml/h, and 0.7 MPa, respectively. Moreover, the TE, MR, and RN are decreased by 12.2%, 14.2%, and 42.5%, respectively. The reductions in the MR and RN of the burnished surface are 90.23% and 88.18%, respectively, as compared to the pre-machined conditions.

PERMS: An efficient rescue route planning system in disasters

The occurrence of natural and man-made disasters usually leads to significant social and economic disruption, as well as high numbers of casualties. Such occurrences are difficult to predict due to the huge number of parameters with mutual interdependencies which need to be investigated to provide reliable predictive capabilities. In this work, we present high-Performance Emergent Rescue Management e-System (PERMS), an efficient rescue route planning scheme operating within a high-performance emergent rescue management system for vehicles based on the mobile cloud computing paradigm. More specifically, an emergency rescue planning problem (ERRP) is investigated as a multiple travelling salesman problem (MTSP), as well as a novel phased heuristic rescue route planning scheme. This consists of an obstacle constraints and task of equal division-based K-means++ clustering algorithm (OT-K-means++), which is more suitable for clustering victims in disaster environments, and a glow-worm swarm optimisation algorithm based on chaotic initialisation (GSOCI), which provides the appropriate rescue route for each vehicle. A prototype is developed to evaluate the performance of this proposed approach, which demonstrates a better performance compared to other well-known and widely used algorithms.As demonstrated by the validation process, our approach enhances the accuracy and convergence speed for solving the emergency rescue planning problem. Furthermore, it shortens the length of the rescue route, as well as rescue time, and it leads to reasonable and balanced allocation of emergency rescue tasks, whilst achieving an overall efficient rescue process. More specifically, by considering scenarios with 200 victims, compared with K-means and K-means++, OT-K-means++ reduces the time cost of clustering by 9.52% and 17.39% respectively, and reduces the number of iterations by 11.11% and 15.78% respectively. Compared with ACO or GA, GSOCI reduces the length of rescue route by 9.81% and 16.36% respectively, and reduces the time of rescue by 4.35% and 15.38% respectively.

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

Glowworm Swarm Optimization Algorithm- and K-Prototypes Algorithm-Based Metadata Tree Clustering

The main objective of this paper is to present a new clustering algorithm for metadata trees based on K-prototypes algorithm, GSO (glowworm swarm optimization) algorithm, and maximal frequent path (MFP). Metadata tree clustering includes computing the feature vector of the metadata tree and the feature vector clustering. Therefore, traditional data clustering methods are not suitable directly for metadata trees. As the main method to calculate eigenvectors, the MFP method also faces the difficulties of high computational complexity and loss of key information. Generally, the K-prototypes algorithm is suitable for clustering of mixed-attribute data such as feature vectors, but the K-prototypes algorithm is sensitive to the initial clustering center. Compared with other swarm intelligence algorithms, the GSO algorithm has more efficient global search advantages, which are suitable for solving multimodal problems and also useful to optimize the K-prototypes algorithm. To address the clustering of metadata tree structures in terms of clustering accuracy and high data dimension, this paper combines the GSO algorithm, K-prototypes algorithm, and MFP together to study and design a new metadata structure clustering method. Firstly, MFP is used to describe metadata tree features, and the key parameter of categorical data is introduced into the feature vector of MFP to improve the accuracy of the feature vector to describe the metadata tree; secondly, GSO is combined with K-prototypes to design GSOKP for clustering the feature vector that contains numeric data and categorical data so as to improve the clustering accuracy; finally, tests are conducted with a set of metadata trees. The experimental results show that the designed metadata tree clustering method GSOKP-FP has certain advantages in respect to clustering accuracy and time complexity.

Optimal 5G network slicing using machine learning and deep learning concepts

Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like “user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type.” Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like “eMBB, mMTC, and URLLC” for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing.

A hybrid approach for cluster head determination of unmanned aerial vehicle in flying ad-hoc networks

In recent years, flying ad hoc networks (FANETs) have witnessed a notable increase in its applications after the onset of the collaborations of the small unmanned aerial vehicles (UAVs). Because of its inherent characteristics, FANETs are used in diverse application ranging from the military to civil domain. Conversely, there are certain issues pertaining to the communication among the UAVs in view of the high mobility and limited battery resources available in the UAVs, resulting in their short lifetime. The paper is an attempt to address these issues plaguing to the short lifespan of the UAVs. In this paper, we propose a hybrid bio-inspired algorithm HGSOFA for optimizing cluster head (CH) selection in a FANETs. HGSOFA utilizes the hybrid implementation of glowworm swarm optimization (GSO) and firefly algorithm (FA). In this paper, we explain the step-by-step working of the HGSOFA and then performance is evaluated through rigorous simulations. Two separate network areas with varying node density is considered for conducting all the simulations. A robust experimental environment is developed using Taguchi and orthogonal methods. HGSOFA’s performance is tested against the conventional GSO and FA algorithms in respect of cluster building time, energy consumption and first node death. Comparable results have showcased the advantages of the HGSOFA as compared to other algorithms.

Energy-efficient cluster head selection algorithm for IoT using modified glow-worm swarm optimization

Internet of Things (IoT) is a new age technology that connects virtually every IP-enabled things in the world. Because of the constrained nature of the resources, energy-efficient clustering plays a very important role in successful implementation of these networks and also in increasing its lifetime. The proposed methodology provides an adaptive cluster head selection algorithm based on glow-worm swarm optimization algorithm. Most of the existing approaches groups nodes into clusters containing a fixed number of nodes. This is not applicable in certain cases, such as, when many of the nodes are dead. In the proposed approach, the number of nodes in each cluster is not fixed, and it automatically changes according to the number of alive nodes in the network, which increases the lifetime of the network. The proposed approach also ensures the minimum overlapping of the clusters by selecting geographically distributed cluster heads which increases the energy efficiency of the network by minimising the communication overhead. Repeated selection of cluster head also helps in load balancing in the network. We have implemented the proposed algorithm in OMNET++ simulator and demonstrated how it behaves with different densities of sensor deployment and communication ranges. The proposed algorithm is found to work notably well as compared to state-of-the-art IoT clustering protocol even when more than 20% nodes run out of energy. As time progresses, the improvement is even more apt.

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

Clustering algorithm for mixed attributes data based on glowworm swarm optimisation algorithm and K-prototypes algorithm

The main purpose of this research is to improve the clustering accuracy of mixed attributes data. Therefore, glowworm swarm optimisation (GSO) algorithm is introduced into K-prototypes algorithm to...

Clustering algorithm for mixed attributes data based on glowworm swarm optimisation algorithm and K-prototypes algorithm

Clustering algorithm for mixed attributes data based on glowworm swarm optimisation algorithm and K-prototypes algorithm

Cluster-based optimal sink repositioning technique for WSNs using an improved glowworm swarm optimisation and S* position search algorithm

Wireless sensor network is a group of organised sensor nodes that are limited to their resources such as limited bandwidth, energy etc. The data gathered from sensor nodes will be forwarded to the sink node to take proper actions. In order to avoid the failures, the sink nodes should be positioned in the better place for the resource optimisation. In this paper, first we will apply clustering for grouping the nodes that are adjacent to sink node. Clustering is done by an improved glowworm swarm optimisation (IGSO) algorithm. The node which has the shortest path is selected for position. Hence, this approach is used to find the optimal position for all the sinks in order to optimise the lifetime of the network and move according with intelligent sink positioning.

Computation Offloading Based on Improved Glowworm Swarm Optimization Algorithm in Mobile Edge Computing

Mobile edge computing(MEC) has already shown its powerful computing ability in mobile device which can broaden the capacity of computing. However, in view of mobile edge computing system, due to the varying network conditions and limitation of the wireless channel, the problem of long delay and high energy consumption maybe increase in the industrial production line. Therefore, computing offloading was proposed to reduce delay and energy consumption in mobile edge computing but the task allocation of offloading decision still can be improved by using different optimization algorithms. In this paper, we improved computation offloading method by an improved glowworm swarm optimization(GSO) algorithm to solve this problem for multi-user-multi-MEC in mobile edge computing. Compared with existing improved algorithms of computation offloading, the experimental results show that our proposal can reduce the system cost a lot(to 25%) which has a better performance of saving energy and reducing delay in the mobile edge computing environment.

Speed Control of Motor Based on Improved Glowworm Swarm Optimization

To better regulate the speed of brushless DC motors, an improved algorithm based on the original Glowworm Swarm Optimization is proposed. The proposed algorithm solves the problems of poor robustness, slow convergence, and low accuracy exhibited by traditional PID controllers. When selecting the glowworm neighborhood set, an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution. After the glowworm’s position is updated, the league selection operator is introduced to search for the global optimal solution. Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm. The mathematical model of the brushless DC motor is established, the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm, and the speed of the brushless DC motor is adjusted. In a Simulink environment, a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor, and this simulation was compared with a traditional PID control. The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.

Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm.

Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.

Quasi Oppositional Glowworm Swarm Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

Wireless sensor network (WSN) becomes popular due to its applicability in distinct application areas like healthcare, military, search and rescue operations, etc. In WSN, the sensor nodes undergo deployment in massive number which operates autonomously in harsh environment. Because of limited resources and battery operated sensor nodes, energy efficiency is considered as a main design issue. To achieve, clustering is one of the effective technique which organizes the set of nodes into clusters and cluster head (CH) selection takes place. This paper presents a new Quasi Oppositional Glowworm Swarm Optimization (QOGSO) algorithm for energy efficient clustering in WSN. The proposed QOGSO algorithm is intended to elect the CHs among the sensor nodes using a set of parameters namely residual energy, communication cost, link quality, node degree and node marginality. The QOGSO algorithm incorporates quasi oppositional based learning (QOBL) concept to improvise the convergence rate of GSO technique. The QOGSO algorithm effectively selects the CHs and organizes clusters for minimized energy dissipation and maximum network lifetime. The performance of the QOGSO algorithm has been evaluated and the results are assessed interms of distinct evaluation parameters.