Bird Swarm Algorithm Research Articles

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.

Optimization based routing model for the dynamic path planning of emergency vehicles

With the increasing number of traffic vehicles and urbanization, the frequencies of traffic accidents are growing exponentially. The research for routing emergency vehicles has gained significant attention in Vehicular Ad hoc Network (VANET) topology. The path planning for an emergency vehicle can avoid congestion and minimize the travel time to prevent accidents. Thus, this work proposes a dynamic path planning scheme for routing emergency vehicles. At first, the VANET is simulated and the dynamic path planning is performed after the arrival of emergency vehicles. The proposed dynamic path planning includes two steps, namely travel-time forecasting, and bi-model routing. The travel time is predicted using the Support Vector Regression model and the bimodal is employed to route the emergency vehicles optimally. The bimodal consists of two steps, such as K-path discovery and optimal path selection process. Initially, the possible K shortest path is detected from which the optimal path is selected by the proposed Exponential Bird Swarm Optimization Algorithm (Exp-BSA). The proposed Exp-BSA algorithm is designed by integrating the Exponentially Weighted Moving Average concept in Bird Swarm Algorithm (BSA). Thus, the proposed Exp-BSA determines the optimal paths based on the devised fitness function, and the selected paths are suggested to emergency vehicles for faster routing. The performance of the proposed dynamic path planning for an emergency vehicle based on Exp-BSA is evaluated using three simulation setups based on the metrics, such as distance, average traffic density, average speed, and travel time. Also, the performance of the proposed method is compared with the existing methods, such as SFLA-KC, K-Path, and BSA. From the results, it is exposed that the proposed Exp-BSA method achieves the overall best performance than the existing methods with a minimal distance of 48.404 m, an average traffic density of 0.015 min, an average speed of 1.392 m/s, travel time of 19.608 s.

Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization

Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).

Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm

Ultra-supercritical units with high steam temperature and pressure have been widely employed in thermal power plants due to their high efficiency and environmental friendliness. To ensure efficient unit operation, it is necessary to develop a model for the boiler-turbine coupled process of the unit. The present models may be complicated or non-transparent, preventing their practical application. In this work, the model structure is a transfer function matrix by dynamic analysis, and the model parameters are obtained by the proposed data-driven multivariable model parameters intelligent identification scheme with the cloud adaptive chaotic bird swarm algorithm combining sheep optimization and lion swarm optimization. By employing operational data from 1000 MW ultra-supercritical unit, extensive experimental results were given to show that the model identified was reasonable and accurate by comparison tests between identification outputs and process outputs around 800 MW operation condition. The developed model can provide reference for further control strategy synthesis and performance optimization.

Fault Diagnosis of Planetary Roller Screw Mechanism Based on Bird Swarm Algorithm and Support Vector Machine

Intelligent fault diagnosis of rotating machinery has been widely developed in recent years due to the improvement of computing power, but how to identify the fault states of planetary roller screw mechanism is a difficult problem in practical industrial applications. A fault diagnosis method for planetary roller screw mechanism is proposed by combining with bird swarm algorithm (BSA) and support vector machine (SVM), which shows strong advantages in solving small sample, nonlinear and high-dimensional identification problems, and the bird swarm algorithm with high optimization accuracy and good robustness. In this paper, the vibration data of the planetary roller screw mechanism in two states with and without grease are collected, and features are extracted from the time domain, frequency domain and time- frequency domain, respectively. The predicted accuracy of SVM and BSA-SVM is compared, and the feasibility of the proposed method is verified.

Swarm intelligent hyperdization biometric

At the last decade the importance of biometrics has been clearly configured due to its important in the daily life that starts from civil applications with security and recently terrorizing. A Footprint recognition is one of the effective personal identifications based on biometric measures. The aim of this research is to design a proper and reliable left human footprint biometrics system addressed (LFBS). In addition, to create a human footprint database which it is very helpful for numerous use such as during authentication. The existing footprint databases were very rare and limited. This paper presents a sturdy combined technique which merges between Image Processing with Artificial Intelligent technique via Bird Swarm Optimization Algorithm (BSA) to recognize the human footprint. The use of (BSA) enhance the performance and the quality of the results in the biometric system through feature selection. The selected features was treated as the optimal feature set in standings of feature set size. The visual database was constructed by capturing life RGB footprint images from nine person with ten images per person. The visual dataset images was pre-processed by successive operations. Chain Code is used with footprint binary image, then statistical features which represent the footprint features. These features were extracted from each image and stored in Excel file to be entered into the Bird Swarm Algorithm. The experimental results show that the proposed algorithm estimates, excellent results with a smaller feature set in comparison with other algorithms. Experimental outcomes show that our algorithm achieves well-organized and accurate result about 100% accuracy in relation with other papers on the same field.

Bayesian-Based Spectrum Sensing and Optimal Channel Estimation for MAC Layer Protocol in Cognitive Radio Sensor Networks

Abstract Cognitive radio (CR) is an intelligent and adaptive radio technology that automatically detects the available channels in the wireless spectrum and sometimes changes the transmission parameters to enable effective communication. Spectrum sensing in CR prevents harmful interference with the licensed users and maximizes the spectrum utilization. Thus, this paper proposes a technique for optimal channel estimation and spectrum sensing for MAC layer protocol in CR networks such that the scheduling issues are addressed. Initially, in the CR networks, spectrum sensing is done using the proposed optimal naive Bayes classifier (ONBC) based on the signal statistics, such as energy and likelihood ratio. The ONBC is developed by integrating the bat–bird swarm algorithm (BBSA) with the naive Bayes classifier, which works based on the Bayesian concept. The BBSA is newly developed by integrating the bird swarm algorithm (BSA) and bat algorithm. Finally, the channel estimation is done using the pilot-based sequential procedure and least square estimation (LSE). The analysis of the proposed method is done in the Rayleigh and Rician environments using 256 and 512 sub-carriers. From the results, it is exposed that the proposed BBSA + LSE pilot-based sequential method obtains the bit error rate, normalized energy and Probability detection (PD) of is 0.0126, 0.8446 and 0.9355, respectively.

Energy Management of a Power System for Economic Load Dispatch Using the Artificial Intelligent Algorithm

Economic Load Dispatch (ELD) is a key issue in power systems and its goal is to achieve minimum economic costs by allocating the output of generator units when satisfying the load demands and the operating constraints. As the dimension of the variables and the constraints increase, the traditional mathematical method is gradually not suitable for the ELD. This paper proposes an Improved Bird Swarm Algorithm (IBSA) to solve the ELD problem of a power system. By introducing the nonlinear cognitive and social coefficients, the proportion of individual learning and social learning of birds can be dynamically adjusted. In addition, the Levy flight strategy is added to the group between producers and beggars to increase the randomness. The performance of IBSA is verified via two systems consisting of 6 and 15 units, respectively, that take into account generation limitation, ramp rate limit, and prohibited operating zones. From the simulation results, the IBSA has shown excellent performance and robustness, which can be considered as a reliable solution for the ELD.

A Collaborative Compound Neural Network Model for Soil Heavy Metal Content Prediction

The prediction of soil heavy metal content is an important part of the management of soil heavy metal pollution, but it is often ignored. At present, there are few studies on the prediction of soil heavy metal content, and it is an urgent problem to choose an efficient method for soil heavy metal content prediction. In this paper, a collaborative compound neural network model (CCNN) was put forward to predict the soil heavy metal content, this model uses wavelet neural network (WNN) as the basic prediction model, and at the same time proposes a parallel bird swarm algorithm (PBSA) to solve the parameter optimization problem of WNN, based on the bird swarm algorithm (BSA), the PBSA not only increases the gathering behavior of individual, but also adopts sine transformation based on fitness difference ratio to carry out the following behavior of beggars to improve the global optimization ability, besides that, the acceptance criterion is used to compare the fitness of individuals after updating to avoid falling into a local optimum. Soil heavy metal content data from Yinchuan city of Ningxia and six new urban areas in Wuhan, China are used to make prediction experiments respectively, through compare with support vector machine (SVM), radial basis function neural network (RBFNN), WNN and bird swarm algorithm optimizes wavelet neural network (BSA-WNN), the experimental results demonstrate that the predicted value of the CCNN is closer to the actual value and has better prediction performance.

Cathodic copper plate surface defect detection based on bird swarm algorithm with chaotic theory

Cathodic copper plate surface defect detection based on bird swarm algorithm with chaotic theory

Hybrid Optimization Driven Technique for Malicious Javascript Detection Based on Deep Learning Classifier

The growth of the web users and thecontents are increasing in a daily basis. In all these webpages the implementation of javascripts are a common factor. These scripts are used for the simplicity and achieve interaction with the user, but, also could be used to harm the end user by stealing information, redirecting to phishing pages and installing harmful softwares. This alarms an immediate look into the security concerns of the javascript. There exist many machine learningbased malicious script detection approaches, but majority of them follow a shallow discriminating models where manual definition of features are constructed with artificial rules. In this paper, a deep learning framework for detecting malicious JavaScript code is proposed combing the optimization power of Bird Swarm Algorithm. To extract high-level features from JavaScript code Stacked denoising auto-encoders are implemented and BSA is used to optimise the features and identify the malicious codes. The theoretical model [2] have an accuracy of 94% in identifying the malicious codes.

Parameter estimation for fractional-order chaotic systems by improved bird swarm optimization algorithm

The essence of parameter estimation for fractional-order chaotic systems is a multi-dimensional parameter optimization problem, which is of great significance for implementing fractional-order chaos control and synchronization. Aiming at the parameter estimation problem of fractional-order chaotic systems, an improved algorithm based on bird swarm algorithm is proposed. The proposed algorithm further studies the social behavior of the original bird swarm algorithm and optimizes the foraging behavior in the original bird swarm algorithm. This method is applied to parameter estimation of fractional-order chaotic systems. Fractional-order unified chaotic system and fractional-order Lorenz system are selected as two examples for parameter estimation systems. Numerical simulation shows that the algorithm has better convergence accuracy, convergence speed and universality than bird swarm algorithm, artificial bee colony algorithm, particle swarm optimization and genetic algorithm.

On-Off Control of Range Extender in Extended-Range Electric Vehicle using Bird Swarm Intelligence

The bird swarm algorithm (BSA) is a bio-inspired evolution approach to solving optimization problems. It is derived from the foraging, defense, and flying behavior of bird swarm. This paper proposed a novel version of BSA, named as BSAII. In this version, the spatial distance from the center of the bird swarm instead of fitness function value is used to stand for their intimacy of relationship. We examined the performance of two different representations of defense behavior for BSA algorithms, and compared their experimental results with those of other bio-inspired algorithms. It is evident from the statistical and graphical results highlighted that the BSAII outperforms other algorithms on most of instances, in terms of convergence rate and accuracy of optimal solution. Besides the BSAII was applied to the energy management of extended-range electric vehicles (E-REV). The problem is modified as a constrained global optimal control problem, so as to reduce engine burden and exhaust emissions. According to the experimental results of two cases for the new European driving cycle (NEDC), it is found that turning off the engine ahead of time can effectively reduce its uptime on the premise of completing target distance. It also indicates that the BSAII is suitable for solving such constrained optimization problem.

Improved Intrusion Detection System with Optimization Enabled Deep Neural Networks

Cyber-crimes are prevailing at the extreme in the today’s technical world as the massive usage of the internet is on the peak among the world users, raising the security and privacy concerns. Thus, the paper concentrates on the intrusion detection mechanism in the networks, which is performed using the optimization-based deep belief neural networks (DBN). Input data is classified using the DBN classifier and the complexity associated with the classification is relieved through the feature selection strategy for which the Bhattacharya distance is employed. The DBN training is performed using Levenberg–Marquardt (LM) algorithm and Bird Swarm Algorithm (BSA), which is decided based on the minimal mean square error. The intrusion detection affords the security and privacy to the data. The analysis of the methods is presented using the KDD cup dataset and the comparative analysis is performed based on the accuracy, sensitivity, and specificity. The accuracy, sensitivity, and specificity of the BSA-DBN approach of intrusion detection are found to be 96.45%, 94.07%, and 96%, respectively.

Optimal Sitting and Sizing of Distributed Generation Units in an Indian Practical Distribution System using Bird Swarm Algorithm

Optimal Sitting and Sizing of Distributed Generation Units in an Indian Practical Distribution System using Bird Swarm Algorithm

An Efficient Precoding Algorithm for mmWave Massive MIMO Systems

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.

Recognition of polycyclic aromatic hydrocarbons using fluorescence spectrometry combined with bird swarm algorithm optimization support vector machine.

Polycyclic aromatic hydrocarbons (PAHs), known as a widespread toxic pollutants in aquatic environments, have caused enormous harm to human society and even the earth's ecology. Therefore, it is necessary to identify PAHs pollutants accurately and efficiently. In this work, the binary mixed solvents Acenaphthylene and Fluorene (ANP-FLU), Acenaphthylene and Naphthalene (ANP-NAP), FLU-NAP representing typical PAHs mixtures in aqueous solution were identified by using three-dimensional fluorescence spectroscopy and machine learning intelligent algorithm. The fluorescence spectroscopy was used to analyze the similarity and difference of ANP, FLU, NAP and the mixtures of two above compounds. What's more, bird swarm algorithm optimization support vector machine (BSA-SVM), introduced as a new method, was proposed to identify PAHs. In order to verify the accuracy of the BSA-SVM algorithm, the BSA-SVM, particle swarm optimization support vector machine (PSO-SVM), genetic optimization support vector machine (GA-SVM) and SVM algorithms were test by processing the same spectral data. The test set classification accuracy of BSA-SVM can reach 100%, which was higher than that of PSO-SVM, GA-SVM and SVM. Moreover, with the exception of the original SVM model, the training speed of BSA-SVM was the fastest among the three optimization algorithms. The satisfying results demonstrated that the BSA-SVM was more suitable for qualitative analysis of PAHs.

A Novel Improved Bird Swarm Algorithm for Solving Bound Constrained Optimization Problems

Bird swarm algorithm (BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.

Application of Bird Swarm Algorithm for Allocation of Distributed Generation in an Indian Practical Distribution Network

Application of Bird Swarm Algorithm for Allocation of Distributed Generation in an Indian Practical Distribution Network