Bird Swarm Research Articles

Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic data by the use of min-max normalization technique. Besides, OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases. For enhancing the predictive outcomes of the OCNN-ALSTM technique, the bird swarm algorithm (BSA) is employed to it and thereby overall efficacy of the network gets improved. The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work. The experimental validation of the OCNN-ALSTM technique is performed using benchmark datasets and the results are examined under several aspects. The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.

Battery parameter identification strategy based on modified coot optimization algorithm

Coot algorithm (COOT) is a new metaheuristic algorithm (MA) based on the swarm of COOT birds' behavior. Like other MAs, the COOT algorithm may easily become trapped in local optima, tends to low diversity, slow convergence speed, and imperfect balance between exploitation/exploration. Thus, to overcome these shortcomings of the original COOT, seven effective strategies are injected into COOT, such as - best agent guide, control randomization, Transition Factor (TF), adjusting the position, leading the group towards the optimal area, phasor operator and Opposition Based Learning (OBL). To be specific, to alleviate these shortcomings, a new modified Coot algorithm (COOT) called mCOOT algorithm is proposed. To investigate the efficiency of mCOOT, it has been verified on complex and standard test suite given in IEEE CEC’2017 and its performance are compared with seven stable well-known MAs. In addition, the Wilcoxon sign rank test has been used to check the results’ significance. To ensure the robustness of mCOOT, it has been used to identify the optimal lithium-ion (Li-ion) battery model parameter. Battery parameters are unmeasured parameters that can only be estimated. The main advantage of this identification algorithm is the high accuracy where the root mean square error (RMSE) has been reduced to 7.11548 × 10–4 with an efficiency of 99.25% and STD of 0.59466 × 10–5. Thus, we observed that mCOOT algorithm can be used as an efficient algorithm to solve electrical and complicated optimization problems.

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

Effect of partial loading on a three‐area hydro‐thermal system integrated with realistic dish‐Stirling solar thermal system, accurate model of high‐voltage direct link considering virtual inertia and energy storage systems

This article presents the studies of automatic generation control (AGC) on a three-area hydro-thermal system under various loading conditions. The study at various loading conditions is investigated because the model parameter of steam and hydro depend on their generation schedule. A maiden attempt has been made to design a three degree of freedom tilt integral derivative (3DOF-TID) controller as a secondary controller. Its gains are optimized using a novel bird swarm algorithm (BSA). Comparison of dynamic responses in a hydro-thermal system with the proposed 3DOF-TID reveals better performance over 2DOF-TID, 2DOF-PID controllers. It is also explored that the dynamic system responses are improved in thermal power plants and deteriorate in the hydropower plants when loading increases from its nominal values. Moreover, the dynamic system responses are analysed with the realistic dish-Stirling solar thermal system (RDSTS) considering fixed and random solar insolations. The comparative analysis suggests that RDSTS with fixed insolation enhances system dynamics by 5.72% over random insolation. Further, the comparison of dynamic responses of the system with AC tie-line and parallel AC/accurate high-voltage direct (AHVDC) tie-lines reveals that the system dynamics are improved with the parallel AC/AHVDC tie-lines. Furthermore, the inertia is emulated in the parallel AC/AHVDC tie-line using capacitive energy storage (CES), super magnetic energy storage (SMES) systems, and redox flow battery (RFB). Simulations show that the inertia emulated hydro-thermal-RDSTS system with RFB provides better dynamics. Sensitivity analysis suggests that 3DOF-TID parameters values at nominal condition are appropriate for higher value of disturbance without need for optimization.

Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network

This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (PCA), which is an arithmetical process that exploits an orthogonal transformation to exchange a group of annotations of probably interrelated constraints. The classification procedure is performed using neural network (NN), with the new training algorithm called bird swarm algorithm, which is modified based on probability and hence termed as probability-based BSA (P-BSA).

Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity

The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.

Research on the Control Method of Short-term Transmission and Distribution Price Level

In order to ensure the orderly competition in the power market, through a variety of measures to reduce the electricity cost of enterprises and users, and improve the service quality of power enterprises, this paper puts forward the research on the control method of the total level of short-term transmission and distribution price. Combined with the principle of “three sets and five big”, the power grid marketing system is established, the main responsibilities of customer service center are assigned, and the main factors affecting pricing are extracted by bird swarm algorithm. Combining the rate of return on investment with the price cap regulation mode, a growth type short-term total level regulation mode of transmission and distribution electricity price is constructed. Simulation results show that the proposed control method has high accuracy in forecasting electricity demand and can give full play to the role of market price regulation.

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy.

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.

Hybrid Bird Swarm Optimized Quasi Affine Algorithm Based Node Location in Wireless Sensor Networks

Hybrid Bird Swarm Optimized Quasi Affine Algorithm Based Node Location in Wireless Sensor Networks

Multiple sequence alignment using enhanced bird swarm align algorithm

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

Pantograph-catenary contact reliability analysis under crosswind

PurposeThis paper aims to establish the relationship between crosswind speed and pantograph-catenary lateral deviation, as well as quantify the influence of crosswind speed and rod size uncertainty on pantograph-catenary contact reliability.Design/methodology/approachThe closed vector method is used to establish the pantograph-catenary kinematics formula. A new prediction model is proposed by using the bird swarm algorithm to optimize the grey model. The lateral deviation of the pantograph and catenary is predicted via the new model. Then the relationship between the effective length of the rod and operating mileage is inferred by combining the effective length theory with the Gamma process, as well as the pantograph-catenary contact reliability model is established according to reliability theory.FindingsThe results obtained show the impacts of uncertainty design parameters of pantograph rods on pantograph-catenary contact reliability index, and the results at crosswind speed of 0 ms−1 and 5 ms−1 are 5.0630 and 4.1442, respectively. The reliability decreases with the increasing crosswind speed, and can be greater than the reliability calculated for rod size degradation due to long-term use.Originality/valueMost preceding works on pantograph-catenary contact reliability were based on principles of dynamics, without considering the pantograph-catenary relative motion. This research reveals the law of pantograph-catenary relative motion for uncertainty design parameters and crosswind, and quantifies the reliability from the angle of kinematics.

Intelligent Three-dimensional Node Localization Algorithm Using Dynamic Path Planning

Background: With the applications of wireless sensor network technology, threedimensional node location technology is crucial. The process of node localization has some disadvantages, such as the uneven distribution of anchor nodes and the high cost of the network. Therefore, the mobile anchor nodes are introduced to effectively solve accurate positioning. Objective: Considering the estimated distance error, the received signal strength indication technology is used to optimize the measurement of the distance. At the same time, dynamic stiffness planning is introduced to increase virtual anchor nodes. The bird swarm algorithm is also used to solve the optimal location problem of nodes. Method: Firstly, the dynamic path is introduced to increase the number of virtual anchor nodes. At the same time, the improved RSSI distance measurement technology is introduced to the node localization. Then, an intelligent three-dimensional node localization algorithm based on dynamic path planning is proposed. Finally, the proposed algorithm is compared with similar algorithms through simulation experiments. Results: Simulation results show that the node coordinates obtained by the proposed algorithm are more accurate, and the node positioning accuracy is improved. The execution time and network coverage of the algorithm are better than similar algorithms. Conclusion: The proposed algorithm significantly improves the accuracy of node positioning. However, the traffic of the algorithm is increased. A little increase in traffic in exchange for positioning accuracy is worthy of recognition. The simulation results show that the proposed algorithm is robust and can be implemented and promoted in the future.

A Fusion Crossover Mutation Sparrow Search Algorithm

Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SSA to keep the diversity of the population and reduce the probability of falling into local optimum; Secondly, we creatively introduce the idea of crossover and mutation in genetic algorithm into SSA to get better next-generation population. These two improvements not only keep the diversity of the population at all times but also make up for the defect that the sparrow search algorithm is easy to fall into local optimum at the end of the iteration. The optimization ability of the improved SSA is greatly improved.

Privacy and Utility-Assisted Data Protection Strategy for Secure Data Sharing and Retrieval in Cloud System

ABSTRACT The outsourcing of Electronic Health Records (EHR) on cloud infrastructures has enabled medical data sharing among several healthcare applications. The blockchain offers security by authenticating users with encryption methods. The collaboration with the cloud provides better management but poses threats to the privacy of the patient. This paper devises a novel blockchain-assisted framework for effective data sharing and retrieval using cloud platforms. Here, the data protection model is devised in EHR application for secure transmission. The entities in the cloud platform include data user, data owner, smart agreement, transactional blockchain, and Inter-Planetary File System (IPFS). Here, the data owner includes a data protection model to secure EHR in which secured EHR is transferred to IPFS before sharing with the data user. The data protection is done by preserving data privacy using Tracy-Singh product and proposed Conditional Autoregressive Value at risk (CAViaR)-based Bird swarm algorithm (CAViaR-based BSA) combination of BSA and CAViaR for generating optimal privacy-preserving coefficients. The objective function is newly devised considering privacy and utility. The proposed CAViaR-based BSA outperformed other methods with minimal responsiveness of 251.339 s, maximal genuine user detection of 32.451%, maximal privacy of 96.5%, and minimal information loss of 3.5%.

A new optimization method based on COOT bird natural life model

Recently, many intelligent algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search volatile and multi-dimensional solution spaces and find optimal answers timely. In this paper, a new meta-heuristic method is proposed that inspires the behavior of the swarm of birds called Coot. The Coot algorithm imitates two different modes of movement of birds on the water surface: in the first phase, the movement of birds is irregular, and in the second phase, the movements are regular. The swarm moves towards a group of leading leaders to reach a food supply; the movement of the end of the swarm is in the form of a chain of coots, each of coot which moves behind its front coots. The algorithm then runs on a number of test functions, and the results are compared with well-known optimization algorithms. In addition, to solve several real problems, such as Tension/Compression spring, Pressure vessel design, Welded Beam Design, Multi-plate disc clutch brake, Step-cone pulley problem, Cantilever beam design, reducer design problem, and Rolling element bearing problem this algorithm is used to confirm the applicability of this algorithm. The results show that this algorithm is capable to outperform most of the other optimization methods. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/89102-coot-optimization-algorithm.

Deep regularized variational autoencoder for intelligent fault diagnosis of rotor–bearing system within entire life-cycle process

The performance of complex rotor–bearing system usually decreases with the development of the running time, which indicates that the rotor–bearing system usually goes through several stages (e.g., normal, slight fault, middle fault, and severe fault) in performance degradation process. Namely, the fault categories and severities of rotor–bearing system are difficult to identify in entire life-cycle, which indicates that traditional methods are insufficient in solving such problems. Hence, this paper proposes a novel deep learning model named deep regularized variational autoencoder (DRVAE) for intelligent fault diagnosis of rotor–bearing system. Within the new model, the regular terms are respectively appended to the loss function of variational autoencoder (VAE) through several regularized techniques (e.g., Laplacian, L12 norm and homotopy regularization), which can solve the overfitting problem of the original VAE and enhance feature learning capability of network model. The weighted operation of deep features learned from the regularized VAE is conducted to capture more discriminative fault information, and the hyper-parameters of DRVAE are determined adaptively by bird swarm algorithm (BSA), thus enable the DEVAE method to identify automatically fault categories and severities of rotor–bearing system in entire life-cycle. The effectiveness of the proposed method is validated applying two cases of entire life-cycle test of rotor–bearing system. Experiments show that the proposed method achieves a satisfactory identification result for fault categories and severities of rotor–bearing system. More importantly, the proposed method can greatly improve identification accuracy and feature learning performance compared with the original VAE and most representative several deep learning models.

BSO feature selection based machine learning solar radiation prediction

Benefits of solar power production is constantly increasing to the electrical power grid. Renewable energy sources are becoming alternatives for energy resource around the world. In order to reduce environmental pollution and CO2 emissions, an ideal solution is provided to overcome the energy crisis. Renewable energy forecasting improves the accuracy and significantly improved by developing more solar forecasting models using numerical weather predictions. The solar radiation value reaching the system is very important in determining the energy production potential of the solar energy system. In this, we discuss the development of the project with machine learning combined with multiple metrological models to improve the accuracy of solar radiation forecasting. To implement combination of two models, Bird Swarm Optimization algorithm for select features and for classification Convolutional Neural Network is used. CNN is a system prediction which are including numerous atmospheric based on satellite images or several other weather prediction products.

Spider bird swarm algorithm with deep belief network for malicious JavaScript detection

JavaScript is employed in vast scenarios like web applications, NodeJS, and hybrid-mobile applications. Moreover, JavaScript is a core component in the social network because of its outstanding cross-platform. However, the flexibility of JavaScript made these applications more prone to attacks that induce malicious behaviors in the code. This paper proposes a hybrid optimization model, namely the Spider-based Bird swarm algorithm (S-BSA) algorithm for malicious JavaScript detection. The proposed S-BSA is designed by the integration of Spider Monkey Optimization (SMO) and Bird Swarm algorithm (BSA). The attributes like Boolean, execution time, function calls, break statements, and conditional statements are considered from the datasets. After the extraction of features, Box-Cox transformation is applied to normalize data. Moreover, a Deep belief network (DBN) is employed to classify normal and malicious JavaScript codes. The classification is progressed with DBN, wherein the training of DBN is performed with the proposed S-BSA. The proposed S-BSA algorithm outperformed other methods with maximal accuracy of 0.944, maximal TPR of 0.958, and minimal FPR of 0.081.

A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm

The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.

Impact of PLL and Virtual Inertia on Deregulated AGC System Integrated with Parallel AC/HVDC

This article investigates the impact of phase-locked loop (PLL) systems along with virtual inertia on system dynamics of a parallel AC/HVDC tie-line integrated two-area automatic generation control (AGC) system under a deregulated scenario. The multi-area AGC system comprises two thermal and hydro-gas in area-1, 2. An attempt is made to utilize a new secondary controller named the two-degree-of-freedom tilt-integral-derivative (2DOF-TID) controller. Its gains and PLL parameters are optimized by a novel meta-heuristic algorithm called a bird swarm algorithm (BSA). The system dynamics corresponding to the proposed 2DOF-TID are compared with 2DOF-PID, 2DOF-PI explored better system dynamics with a 2DOF-TID controller. The system dynamics are compared with AC alone, AC/HVDC, AC/HVDC tie-line incorporating with PLL, virtual inertia revealed the better performance of the latter. Moreover, the effect of variation in PLL parameter on the system dynamics are also analyzed and studies suggested that the obtained response at the nominal value of natural frequency (ωn ) and damping coefficient (ζ) reduces the subsequent oscillation and provides better dynamics other than varying condition.