Bird Swarm Algorithm Research Articles

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%.

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.

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.

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.

Atom Taylor Bird Swarm algorithm-based deep belief network for incremental classification using medical data

Medical data classification is considered as a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but to achieve higher performance in terms of classification accuracy result in a great challenge in the medical sector due to the presence of missing values, uncertainty and redundant attributes. Hence, an effective and optimal data classification method named Atom Taylor Bird Swarm Algorithm-based Deep Belief Network (Atom Taylor BSA-based DBN) is proposed in this research to perform incremental classification using medical data. The proposed Atom Taylor BSA is designed by integrating the atom search optimization (ASO) with the Taylor Bird Swarm algorithm (Taylor BSA), which is the development of the Taylor series with BSA. The DBN classifier effectively performs the incremental classification using medical data with its associated neurons and generates optimal result based on the fitness measure. Accordingly, the selected features enable the classifier to increase the performance of classification accuracy. However, the proposed Atom Taylor BSA-based DBN obtained better performance using the metrics, like specificity, sensitivity, and accuracy with the values of 0.8307, 0.9074, and 0.8804, using Hungarian dataset.

The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of -5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.

Linear Active Disturbance Rejection Control for Double-Pendulum Overhead Cranes

Because of the complexity, non-linearity, and under-actuated features of double pendulum overhead cranes, a control method based on linear active disturbance rejection control (LADRC) and differential flatness theory is proposed to realize accurate trolley positioning and effective swing eliminating. Specifically, in order to simplify the system model, we introduce the differential flatness theory to construct system output. Based on this method, the uncertainty and system external disturbance become total disturbance. The control method of the crane is designed according to LADRC. Then, we use the bird swarm algorithm (BSA) to optimize controller’s parameters. Finally, the double-pendulum crane control method based on the LADRC can better accomplish the crane’s anti-swing and location in the real environment according to simulation and experimental results, which confirms its effectiveness and robustness in existing system.

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System.

When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.

Solving Multi-Objective Problems Using Bird Swarm Algorithm

This paper introduces an effective method by combining the multi-objective technique with the bird swarm algorithm (BSA) to obtain a new method called MBSA. The MBSA obtains some of the different non-dominated techniques that maintain variety amongst the optimal solutions. To verify and evaluate the effectiveness of the MBSA, collections of constrained, unconstrained, and engineering problems are measured. These problems have various Pareto front (PF) properties, including non-convex, convex, and discrete PFs. The results show that the MBSA has a good ability to obtain both a better solution spread and better convergence near the true PF. Furthermore, the quantitative and qualitative results indicate that the MBSA provides high convergence and good results in all experiments and with real-world problems against well-known algorithms in the literature.

Cloud Computing Task Scheduling based on Improved Bird Swarm Algorithm

Cloud Computing Task Scheduling based on Improved Bird Swarm Algorithm

A novel hybrid approach for feature selection in software product lines

Software Product Line (SPL) customizes software by combining various existing features of the software with multiple variants. The main challenge is selecting valid features considering the constraints of the feature model. To solve this challenge, a hybrid approach is proposed to optimize the feature selection problem in software product lines. The Hybrid approach ‘Hyper-PSOBBO’ is a combination of Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO) and hyper-heuristic algorithms. The proposed algorithm has been compared with Bird Swarm Algorithm (BSA), PSO, BBO, Firefly, Genetic Algorithm (GA) and Hyper-heuristic. All these algorithms are performed in a set of 10 feature models that vary from a small set of 100 to a high-quality data set of 5000. The detailed empirical analysis in terms of performance has been carried out on these feature models. The results of the study indicate that the performance of the proposed method is higher to other state-of-the-art algorithms.

Performance of swarm intelligence based chaotic meta-heuristic algorithms in civil structural health monitoring

Whale Optimization Algorithm, Eagle Perching Optimization, Dragonfly Algorithm, Flower Pollination Algorithm, Bird Swarm Algorithm (BSA) and Firefly Algorithm (FA), are few of the Swarm-Intelligence based optimization techniques that have been developed by researchers and tested on benchmark functions only and have not been explored for real-life structural health monitoring (SHM). This paper deals with the use of these six algorithms for SHM on real-life quarter-scaled ASCE-Benchmark structure using stiffness-based objective function. It is observed that the performances of all the algorithms are smooth except for BSA and FA due to increased randomness and entrapment in local optima. Hence to improve their performances, modification has been introduced using the chaotic maps in the foraging behaviour of BSA and randomized movement of FA. With the proposed chaotic modifications, it is observed that Chaotic BSA and FA shows good accuracy in damage analysis, with 95% of damage results falling well-within the acceptable range.

Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation

JavaScript is a scripting language that is commonly used in the web pages for providing dynamic functionality in order to enhance user experience. Malicious JavaScript in webpages on internet is an important security issue due to their potentially and universality severe impact. Finding the malicious JavaScript is usually more difficult and time-consuming task in the research community. Hence, an adaptive spider bird swarm algorithm-based deep recurrent neural network (adaptive SBSA-based deep RNN) is proposed for detecting the malicious JavaScript codes in web applications. However, the proposed adaptive SBSA is designed by integrating the adaptive concept with the bird swarm algorithm (BSA) and spider monkey optimization (SMO). With the deep RNN classifier, the complexity issues exists in detecting the malicious codes is effectively resolved through the process of hierarchical computation. Due to the efficiency of the proposed approach, it can evaluate under large real-life datasets.

Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis

Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection, and can be utilized for handling high dimensional data. Then, the selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively.

A Comparative Study of Recent Optimization Methods for Optimal Sizing of a Green Hybrid Traction Power Supply Substation

Although there is a premise that electric trains are zero-emission, their source of energy (fossil fuel power plants) pollutes the air in a place far from the consuming area (traction power supply substation). On the other hand, the price of generating electricity from fossil fuel resources has risen in the aftermath of their ever-decreasing sources. These two economic-environmental factors have caused Hybrid Renewable Energy Sources (HRESs) to be introduced as an alternative to fossil fuel ones. This paper proposes the concept of Green Hybrid Traction Power Supply Substation (GHTPS), that is, using renewable energy resources to meet a traction substation. To find the best size of HRES components having a minimum Lifecycle cost; an optimization method is essential. For this reason, a comparative study on the application of recent optimization methods is employed to find the optimum size of the proposed grid-connected PV/wind turbine traction substation. The optimization methods are: the Atom search optimization (ASO), Harris Hawks Optimization (HHO), Coyote Optimization Algorithm (COA), Multi-population Ensemble Differential Evolution (MPEDE), Bird Swarm Algorithm (BSA), Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and HOMER software. Finally, a sensitivity analysis shows that increasing in the grid electricity price and decreasing the wind turbines investment cost could make renewable energies more economically competitive in the future. Besides Net Present Cost (NPC), Cost of Energy (COE), Payback Time (PT), and various emissions are studied, all of which verify the efficiency of the proposed system.

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model.

Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior's movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.