Crow Search Research Articles

Modeling of Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction Approach

Industrialization and urbanization air is getting polluted due to human activities. CO, NO, C6H6, etc., are the major air pollutants. The focus of air pollutants in ambient air is controlled by the climatological parameters including wind direction, atmospheric speed of wind, temperature, and humidity. Air pollution prediction is a critical sector where machine learning (ML) technique plays a major role. Its main purpose is to tackle and understand the damaging effects of air pollutants on the environment and human health. By using a range of ML techniques such as neural networks, regression, and decision trees, we could analyze historical data on air quality alongside geographical and meteorological factors. This allows us to design model that could detect patterns and predict pollution levels. By taking proactive measures such as providing timely alerts to the public, adjusting controls on emissions, and, implementing strategies to reduce pollution, we can work towards creating healthier and cleaner environments. Embracing the potential of artificial intelligence (AI) in air pollution prediction empowers us to protect the well-being of our communities and make informed decisions. Therefore, this study develops an Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction (ISTSVNI-APP) approach. The major objective of the ISTSVNI-APP technique is to exploit AI concepts with neutrosophic sets (NS) models for the forecasting of air pollution. To do so, the ISTSVNI-APP technique makes use of min-max normalization as the initial preprocessing step. For predicting air pollution, the ISTSVNI-APP technique uses STSVNI approach. To improve the performance of the ISTSVNI-APP technique, modified crow search algorithm (MCSA) is used for the parameter tuning of the STSVNI system. The performance evaluation of the ISTSVNI-APP method is verified utilizing benchmark dataset. The experimental outcomes stated that the ISTSVNI-APP technique gains better performance in predicting air pollution

Synoptic crow search with recurrent transformer network for DDoS attack detection in IoT-based smart homes

Synoptic crow search with recurrent transformer network for DDoS attack detection in IoT-based smart homes

Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges

Abstract Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It is acquired by the progressive demise of the dopaminergic neurons existing in the substantia nigra pars compacta region of the human brain. In the absence of a single accurate test, and due to the dependency on the doctors, intensive research is being carried out to automate the early disease detection and predict disease severity also. In this study, a detailed review of various artificial intelligence (AI) models applied to different datasets across different modalities has been presented. The emotional intelligence (EI) modality, which can be used for the early detection and can help in maintaining a comfortable lifestyle, has been identified. EI is a predominant, emerging technology that can be used to detect PsD at the initial stages and to enhance the socialization of the PsD patients and their attendants. Challenges and possibilities that can assist in bridging the differences between the fast-growing technologies meant to detect PsD and the actual implementation of the automated PsD detection model are presented in this research. This review highlights the prominence of using the support vector machine (SVM) classifier in achieving an accuracy of about 99% in many modalities such as magnetic resonance imaging (MRI), speech, and electroencephalogram (EEG). A 100% accuracy is achieved in the EEG and handwriting modality using convolutional neural network (CNN) and optimized crow search algorithm (OCSA), respectively. Also, an accuracy of 95% is achieved in PsD progression detection using Bagged Tree, artificial neural network (ANN), and SVM. The maximum accuracy of 99% is attained using K-nearest Neighbors (KNN) and Naïve Bayes classifiers on EEG signals using EI. The most widely used dataset is identified as the Parkinson's Progression Markers Initiative (PPMI) database.

Fuzzy Logic Adaptive Crow Search Algorithm for MPPT of a Partially Shaded Photovoltaic System

Fuzzy Logic Adaptive Crow Search Algorithm for MPPT of a Partially Shaded Photovoltaic System

Enhancing Energy Efficiency in IoT-WSN Systems via a Hybrid Crow Search and Firefly Algorithm

Enhancing Energy Efficiency in IoT-WSN Systems via a Hybrid Crow Search and Firefly Algorithm

A new computer‐aided diagnostic method for classifying anaemia disease: Hybrid use of Tree Bagger and metaheuristics

AbstractAnaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision‐support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, six hybrid models are proposed to classify non‐anaemia records, Hgb‐anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search Algorithm (CSA), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia classification problem, fuzzy logic‐based parameter optimization is applied to improve the class‐based accuracy as well as the overall accuracy in the dataset. The proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.

An electric vehicle routing model with charging stations consideration for sustainable logistics

PurposeRecently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.Design/methodology/approachThis study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.FindingsThe result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.Research limitations/implicationsThe optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.Originality/valueIn prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Navigating urban day-ahead energy management considering climate change toward using IoT enabled machine learning technique: Toward future sustainable urban

The rapid evolution of technology and urban population growth has led to a significant increase in energy consumption, particularly in electricity production. However, the ability to meet energy demands, especially during peak periods, is constrained by various factors including environmental concerns, technological limitations, and economic constraints. Effective energy management strategies are crucial to mitigate these challenges and ensure a sustainable energy future. One promising approach is the implementation of demand response programs. In this context, we explore the potential of Home Energy Management Systems (HEMS) leveraging modern Internet of Things (IoT) technology. HEMS systems are at the forefront of research and development in the field of smart grids (SGs), offering capabilities in demand-side management and power availability control. These systems achieve efficient energy usage by optimizing the operation of home appliances (HAs). To further enhance the performance of HEMS, this study introduces a novel metaheuristic known as the Grey Wolf and Crow Search Optimization Algorithm (GWCSOA). This algorithm is employed to determine the optimal scheduling of HAs within the HEMS framework. Additionally, we utilize MATLAB and ThingSpeak modules to implement and test the HEMS design. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs, minimizing the peak-to-average ratio (PAR), and enhancing consumer satisfaction. The findings of this study indicate that the devised system holds the promise of substantially diminishing both power expenses and microgrid emissions. Through the optimization of Home Energy Management Systems (HEMS) operations, this methodology plays a vital role in realizing a sustainable urban energy ecosystem. This aligns seamlessly with the overarching objectives of combating climate change and securing the enduring sustainability of our cities. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs by an average of 25.98 %, minimizing the peak-to-average ratio (PAR) by 30 %, and enhancing consumer satisfaction. The outcomes of this research reveal that the developed system has the potential to significantly reduce both power costs and microgrid emissions. By optimizing HEMS operations, this approach contributes to achieving a sustainable urban energy ecosystem, aligning with the goals of addressing climate change and ensuring the future sustainability of our cities.

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers

AbstractAmong the distribution network planning problems, allocation of transformers is one of the most important and challenging ones. On the other hand, owing to the widespread growth of distributed generations (DGs), their inclusion in the planning problems is vital. This paper proposes an efficient optimization framework for simultaneous allocation of photovoltaic (PV) systems and service transformers in the distribution network. For this aim, to improve the network performance, location and size of the transformers and PV units are optimally determined. Due to the difficulty of the planning problem, four variants of crow search algorithm (CSA) including original CSA, differential CSA (CSAd), directed differential CSA (CSAdd) and chaotic directed differential CSA (CSAcdd) are introduced and applied to the planning problem. To evaluate the impact of PV cost on the results, the planning problem is solved considering different PV system costs. Simulation results show that at the price of $1.25/W, installation of PV systems is cost‐effective, so that over the case study, 400 kW PV system was installed. By decreasing the PV price to $1.2/W, the maximum capacity considered for a PV system is installed. Moreover, on average, CSAd produces more accurate and robust results than the other studied algorithms.

A Hybrid Bald Eagle-Crow Search Algorithm for Gaussian mixture model optimisation in the speaker verification framework

A mutualism of nature-inspired algorithms, the Bald Eagle Search Algorithm (BESA) with the Crow Search Algorithm (CSA), is proposed for Gaussian Mixture Model optimisation in the speaker verification framework. BESA simulates bald eagles’ hunting behaviour, whereas CSA simulates crows’ food-hiding behaviour. BESA excels in exploitation but has limitations in exploration, whereas CSA showcases strong exploration capabilities but faces challenges in effective exploitation. BESA’s hunting characteristic combines crows’ memory update to generate a Hybrid Bald Eagle-Crow Search Algorithm (HBE-CSA). The ability of the proposed HBE-CSA is tested on clean speech utterances from the Librispeech dataset and eight real-life noisy conditions from the AURORA dataset at four different Signal-to-Noise Ratios (SNRs). The comparative analysis shows that the proposed HBE-CSA converges with higher log-likelihood than individual search algorithms and the state-of-the-art Expectation Maximisation algorithm. Detection error trade-off curves validate the results of convergence behaviour, indicating that best speaker models are built using the proposed HBE-CSA. The noise analysis shows the smallest equal error rate for the proposed HBE-CSA at different SNRs than competing algorithms. The proposed HBE-CSA holds promise for optimising models not only in speaker verification but also across other areas of speech processing.

Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach

SummaryDue to the energy crisis and environmental concerns, wind power has seen a considerable increase in use over the past 10 years as a source of renewable energy. Since wind is intermittent and variable, it is obvious that as penetration levels rise, the influence of wind power generation on the electric power system must be considered. Wind power forecasting is essential because large‐scale wind power integration will make it more difficult to plan, operate, and control the power system. An accurate forecast is an efficient way to deal with the operational problems brought on by wind variability. To better utilize wind energy resources, it is essential to increase prediction accuracy. Frequently, the noise in the dataset causes the ramp events to be misclassified or overestimated. The main emphasis of this study is the pre‐processing of wind power data that produces precise time‐series data while reducing noise or artifact content and maintaining the swing feature of the original data. For this task, the data augmentation approach is proposed, where the augmented data (synthetic data) will be added up with the training data, in such a way as to make the strategy useful for real‐time applications. As the next step, the significant features, such as higher‐order statistical features and lower‐order statistical features are extracted. The extracted features act as the input to the recurrent neural network (RNN) classifier, the weights of which are tuned using the proposed honey badger crow (HBCro) optimization algorithm. The proposed HBCro optimization algorithm acts as the major contribution of the proposed model, and it is modeled with the integration of the concepts of the crow search optimization (CSO) algorithm and the honey badger optimization algorithm (HBA). The proposed system is simulated in MATLAB and the effectiveness of the proposed method is validated by comparison with other conventional methods in terms of Error measures. Furthermore, the developed HBCro‐based RNN obtained efficient performance in terms of MSE, MAE, RMSE, RMPSE, MAPE, MARE, MSRE, and RMSRE of 0.1578, 0.0442, 0.2102, 123.238, 124.72, 0.9944, 1.1799, and 1.0732, respectively.

A variegated GWO algorithm implementation in emerging power systems optimization problems

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm.

Variable selection in Inverse Gaussian regression model using modified crow search algorithm

Variable selection in Inverse Gaussian regression model using modified crow search algorithm

Improved Crow Search Algorithm and XGBoost for Transformer Fault Diagnosis

To enhance the accuracy of transformer fault diagnosis, this study proposes an enhanced transformer fault diagnosis model incorporating the Improved Crow Search Algorithm (ICSA) and XGBoost. The dissolved gas analysis in oil (DGA) technique is employed to extract 9-dimensional fault features of transformers as model inputs, in conjunction with the codeless ratio method for training. The output layer utilizes a gradient boosting-based decision tree addition model to obtain the fault diagnosis type. Furthermore, the Golden Sine Algorithm (GSA) is employed for improvement, and the ICSA’s performance is tested by using typical test functions, demonstrating faster convergence and stronger merit-seeking capabilities. The obtained results reveal that the comprehensive diagnostic accuracy of the proposed model reaches 94.4056%, marking an improvement of 8.3916%, 6.2937%, 4.1958%, and 2.0979% compared to the original base XGBoost, PSO-XGBoost, GWO-XGBoost, and CSA-XGBoost fault diagnosis models, respectively. These findings validate the effectiveness of the proposed method in enhancing the fault diagnosis performance of transformers.

Improved Crow Search Algorithm for Solving Quadratic Assignment Problem

Improved Crow Search Algorithm for Solving Quadratic Assignment Problem

Variable selection in Inverse Gaussian regression model using modified crow search algorithm

Variable selection in Inverse Gaussian regression model using modified crow search algorithm

Economic optimization of microgrid with demand response under source-load uncertainty

ABSTRACT The installed capacity of renewable energy producers has increased, which has accelerated the development of the microgrid system. One of the most significant and difficult issues in the field of microgrids is economic optimization. The reliability of the microgrid is threatened by the unpredictability of renewable energy and the variety of load types. In this study, a two-layer microgrid demand response optimization model that takes into account source-load uncertainty. To address the instability of renewable energy and load demand, this study introduces a hybrid scenario reduction strategy that combines Latin Hypercube sampling and probability distance. Then, a two-layer model is developed an upper optimization model that aims to minimize the demand response cost and a lower optimization model that aims to minimize the overall system cost. Finally, the paper then suggests a teaching-learning crow search algorithm that combines the teaching and learning optimization algorithm with the crow search method to resolve the two-layer optimization model. The simulation results demonstrate the effectiveness of the two-layer optimization model and the superiority of the suggested algorithm in optimizing the economic operation of microgrids.

UAV Swarm Mission Planning and Load Sensitivity Analysis Based on Clustering and Optimization Algorithms

Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.

Aplicación del algoritmo de enjambre de salpas al balance de fases en sistemas trifásicos asimétricos

This research addresses the optimal phase-balancing problem by applying a master-slave optimization methodology. The master stage defines the load connections per node using a discrete codification, while the slave stage evaluates each load configuration provided by the master stage via a three-phase power flow. For the master stage, the salp swarm algorithm (SSA) was selected, which is an efficient bio-inspired technique to deal with continuous and discrete nonlinear optimization problems. The slave stage employed the matricial backward/forward power flow method for three-phase asymmetric networks. Numerical simulations in IEEE test feeders composed of 8, 25, and 37 nodes confirm the effectiveness of the SSA approach in finding efficient solutions regarding the expected grid power losses after optimal load phase-swapping. Numerical comparisons with the vortex search algorithm, the Chu & Beasley genetic algorithm, and the crow search algorithm demonstrate the effectiveness of the proposed methodology in dealing with the studied problem. All numerical validations were carried out in the MATLAB programming environment.