Crow Search Research Articles

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.

A Two-Stage Crow Search Algorithm for Solving Optimization Problems

A Two-Stage Crow Search Algorithm for Solving Optimization Problems

Evaluation of oral English teaching quality based on BP neural network optimized by improved crow search algorithm

Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance.

Research on the path of integrating Yan’an spirit into the ideological and political education of contemporary college students based on NDK-means algorithm

Abstract In this paper, the ideological and political education path is designed according to the spirit of Yan’an. In order to verify the feasibility of the path, as well as to make the student evaluation more effective and objective, the article combines the K-means algorithm and proposes a K-means algorithm based on neighborhood spatial density optimization (NDK-means). The algorithm assists in solving the problem of grading the reliability of student assessment and realizes the labeling of student assessment reliability data. Then, based on the student assessment reliability characteristics and data labeling results, a BP neural network prediction algorithm (CSA-BP) based on crow search improvement is proposed to model the student assessment reliability. The results show that the percentages of freshmen to seniors who are satisfied with the Civic Education proposed in this paper are 85.9%, 82.26%, 76.75%, and 72.45%, respectively, in terms of grade level. Students from urban, rural, and rural areas, in terms of their place of origin, all feel satisfied with the current teaching of Civics and Political Science at 80% or more.

Real Time Asthma Disease Detection and Identification Technique from Speech Signals Using Hybrid Dense Convolutional Neural Network

Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Multi-Feature Extraction, Selection with Hybrid Classifiers (MFESHC). Initially, speech signals are preprocessed by using Maximum likelihood estimation based spread spectrum analysis (MLE-SSA) method. Then, Improved prefix Beam Search (IPBS) based natural language processing (NLP) method is used to extract and select the best features from the preprocessed speech signals. Then, hybrid dense convolutional neural networks (HDCNN) are used to classify the type of asthma such as normal, stridor, wheezes and rattle classes. Further, Modified Crow Search (MCS) is used to optimize the losses generated in the HDCNN model. The simulation results shows that the proposed MFESHC method resulted in superior performance as compared to state of art approaches because the MCS effectively reduced the losses in the model.

Optimal selection of cluster head in wireless sensor networks using particle swarm optimization (PSO)

In a wide range of applications, such as the military, healthcare, and environmental monitoring, wireless sensor networks (WSNs) have emerged as a key player. Cluster-based WSNs are a viable method for enhancing the life of the sensor network. Choosing the proper cluster head for wireless sensor networks (WSNs) is a key undertaking that affects the network's performance. Current approaches for selecting the cluster head have a number of drawbacks, such as nodes dying too quickly, uneven energy utilization, and shorter network lifespan. Additionally, conventional techniques like fixed Cluster Head and randomized Clustering are ineffective at extending the network lifetime. In the proposed method, Particle swarm optimization was used to create an optimal cluster head selection that addresses the problem of intra-cluster communication and lowers SN energy consumption. The simulation result shows that the performance improvement of the developed algorithm PSO in terms of network lifetime is 10% against Improved Cuckoo Search Algorithm (ICSA) and 25% against Hybrid Crow Search Algorithm (HCSA), energy consumption is 15% against ICSA and 20% against HCSA, and number of alive node is 4% against ICSA and 6% against HCSA respectively. Therefore, our developed algorithm PSO outperforms ICSA and HCSA in terms of the aforementioned parameters.

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

The need for reliable, state-of-the-art environmental investigations and pioneering approaches to address pressing ecological dilemmas and to nurture the sustainable development goals (SDGs) cannot be overstated. With the power to revolutionize desalination processes, artificial intelligence (AI) models hold the potential to address global water scarcity challenges and contribute to a more sustainable and resilient future. The realm of desalination has exhibited a mounting inclination toward modeling the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process. In this research, the performance of NF–RO based on permeate conductivity was developed using deep learning long short-term memory (LSTM) integrated with an optimized metaheuristic crow search algorithm (CSA) (LSTM-CSA). Before model development, an uncertainty Monte Carlo simulation was adopted to evaluate the uncertainty attributed to the prediction. The results based on several performance statistical criteria (root mean square error (RMSE) and mean absolute error (MAE)) demonstrated the reliability of both LSTM (RMSE = 0.1971, MAE = 0.2022) and the LSTM-CSA (RMSE = 0.1890, MAE = 0.1420), with the latter achieving the highest accuracy. The accuracy was also evaluated using new 2D graphical visualization, including a cumulative distribution function (CDF) and fan plot to justify the other evaluation indicators such as standard deviation and determination coefficients. The outcomes proved that AI could optimize energy usage, identify energy-saving opportunities, and suggest more sustainable operating strategies. Additionally, AI can aid in developing advanced brine treatment techniques, facilitating the extraction of valuable resources from the brine, thus minimizing waste and maximizing resource utilization.

Precision local anomaly positioning technology for large complex electromechanical systems

In recent years, Prognostics Health Management (PHM) technology has become an important reference technology in fields such as avionics and electromechanical systems due to its ability to reduce costs and achieve state based maintenance and autonomous support. However, with the operation of large and complex electromechanical systems (ES), the data generated gradually ages the status of components, and traditional PHM technology is difficult to solve the problem of electromechanical system components becoming more complex. Based on this, this study takes the hydraulic actuator cylinder as an example to construct a local component fault detection model. Firstly, fault data features are extracted using wavelet packet energy spectrum, and then a fault detection model is constructed based on support vector machine (SVM). In response to the shortcomings of SVM, a smooth support vector machine (SSVM) is proposed to replace SVM, and an improved crow search algorithm (ICSA) is used to improve SVM. Finally, an intelligent detection model for hydraulic actuator cylinder faults based on ICSA-SSVM was constructed based on the above algorithms. The experimental results show that the ICSA-SSVM model has the fastest Rate of convergence, among which, the positioning accuracy is 0.96, the fitting degree is 0.984, the fault detection accuracy is 99.16 %, the recall value is 94.52 %, and the AUC value is 0.986, all of which are better than the existing fault detection models. From this, it can be seen that the precise local anomaly localization technology for large-scale complex electromechanical systems based on the ICSA-SSVM algorithm proposed in this study can improve the efficiency and accuracy of fault detection, achieve accurate and intelligent detection of ES local anomalies, and have certain positive significance for the development of China’s industry.

Intelligent proportional-integral-derivate controller using metaheuristic approach via crow search algorithm for vibration suppression of flexible plate structure

Proportional-integral-derivate (PID) controller has gained popularity since the advancement of smart devices especially in suppressing the vibration on flexible structures using different approaches. Such structures required accurate and reliable responses to prevent system failures. Swarm intelligence algorithm (SIA) is one of the optimization methods based on nature that managed to solve real-world problems. Crow search is a well-known algorithm from the SIA group that can discover optimum solutions in both local and global searches by utilizing fewer tuning parameters compared to other methods. Hence, this study aimed to simulate a PID controller tuned by SIA via crow search for vibration cancellation of horizontal flexible plate structures. Prior to that, an accurate model structure is developed as a prerequisite for PID controller development. After the best model is achieved, the proportional-integral-derivative-crow-search (PID-CS) performance was compared to a traditional tuning approach known as the Ziegler Nichols (ZN) to validate its robustness. The result revealed the PID-CS outperformed the proportional-integral-derivative-Ziegler Nichols (PID-ZN) with attenuation values of 44.75 and 42.74 dB in the first mode of vibration for single sinusoidal and real disturbances, respectively. In addition, the value of mean squared error (MSE) for PID-ZN and PID-CS for single sinusoidal disturbance are 0.0167 and 0.0081, respectively. Meanwhile, PID-ZN and PID-CS achieved 2.3981 × 10−4 and 2.3737 × 10−4 when they were exerted with real disturbance. This proves that the PID-CS is more accurate compared to the PID-ZN as it achieved the lowest MSE value.

Reliability constrained dynamic generation expansion planning using honey badger algorithm

Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

Optimal placement of time-varying distributed generators by using crow search and black widow - Hybrid optimization

This paper focuses on the optimal reorganization of radial distribution system (RDS) and the superlative location of time-varying power generation distributed generators, considering time-varying loads. The electric generators, utilizing non-conventional energy sources, are directly connected to the distribution grids with lower ratings compared to conventional source-driven generators. In this study, a combination of optimization techniques, such as the black widow and crow search techniques, is employed to enhance performance in terms of optimal reorganization and superlative location of distribution generation. The reorganization problem is examined and analyzed by considering various scenarios in a standard 69-bus grid, as well as in larger-scale 119-bus and 135-bus systems, with and without the presence of distributed generation resources. The analysis of the proposed optimization techniques with the obtained results is well discussed in this paper.

An optimal multi-objective demand side management of a smart Microgrid consists of various building loads, considering relativity index and uncertainty

This paper addresses microgrid energy management in the presence of distributed generation and active loads while accounting for operational, economic, environmental, and reliability factors. The objective is to minimize the cost of shutdown during N-1 contingencies, projected emission-related expenses, and the overall operational cost of microgrids integrated with distributed generations. A stochastic scheduling technique that optimizes short-term microgrid operation, reducing costs and pollution through renewable resources is introduced. Utilizing demand response programs among residential, commercial, and industrial participants is proposed to counter the uncertainty of renewable resource-generated power. The demand response program implementation suggests considering financial incentives as bid prices, along with energy packages managed by demand response providers. Simulation results demonstrate three strategies to decrease operating costs and pollutants, contrasting scenarios with and without responsive load engagement. Equations governing AC power flow, microgrid operations, reliability, distributed generation, and, demand response programs and batteries, form the analytical core. Stochastic programming addresses uncertainties tied to demand, energy prices, renewable distributed generation production, and microgrid equipment availability. To attain dependable, optimal solutions with dynamic responsiveness, the combined ant lion optimization and crow search algorithm are employed. The proposed method is tested on a sample smart microgrid for validation. Numerical findings unequivocally underscore demand-side management potency in reducing power generation uncertainties from wind turbines and photovoltaics. This paper offers insights into microgrid energy management complexities, paving the way for resilient, cost-effective, and environmentally conscious energy distribution paradigms.

An Optimal Energy Control System for Campus Microgrid Using Crow Search Algorithm Considering Economic Dispatch

This article presents an optimal energy control system that considers economic dispatch (ED) for a campus microgrid to reduce its operating cost. A newly developed crow search algorithm (CSA) is used to enforce the ED in this work. To achieve this purpose, an optimal size of distributed energy resources (DERs) in the campus microgrid is assumed. CSA is used to optimize the energy control system and find the minimum operating cost of the campus microgrid. To indicate the effectiveness of CSA, several scenarios under various load demand conditions in grid-connected and stand-alone microgrid modes are investigated in this work. According to the findings, the suggested model is capable of sufficient power supply in all scenarios and reduces the operating costs more effectively than the reference delineated in the same case. The outcomes confirm that the suggested model’s performance is optimal for the energy control system of a campus microgrid.

Techno‐economic optimization of a hybrid system composed of pumped hydro storage, photovoltaic module, and diesel generator in a multi‐objective framework

AbstractHybridization of photovoltaic (PV) module (as a non‐dispatchable resource), diesel generator (as a dispatchable source), and pumped hydro storage (PHS) (as an energy storage) can provide a promising hybrid energy system (HES). The main outlook of the present study is to develop an efficient multi‐objective framework for optimal design of an off‐grid PV/diesel/PHS HES in which a wide range of parameters related to PHS system is optimized. In the optimization framework, nine decision variables (number of PV modules, number of diesel generators, reservoir installation height, reservoir depth, reservoir diameter, pump's rated power, turbine's rated power, charge pipe diameter, and discharge pipe diameter) are optimized with respect to two objective functions: total net present cost (TNPC) and loss of power supply probability (LPSP). To determine how TNPC is affected by the change of the input variables, a sensitivity analysis is conducted by varying capital cost of PV, PHS, and diesel generator. To obtain a well‐distributed and widely spread Pareto front, a multi‐objective chaotic crow search algorithm (MO‐CCSA) is introduced. Simulation results show that at LPSP = 0% and 5%, levelized cost of energy is around 0.59 and 0.55 $/kWh, respectively. Moreover, variation of the PV capital cost has a significant impact on the TNPC value.

Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization

The Crow Search Algorithm (CSA) is a swarm-based metaheuristic algorithm that simulates the intelligent foraging behaviors of crows. While CSA effectively handles global optimization problems, it suffers from certain limitations, such as low search accuracy and a tendency to converge to local optima. To address these shortcomings, researchers have proposed modifications and enhancements to CSA’s search mechanism. One widely explored approach is the structured population mechanism, which maintains diversity during the search process to mitigate premature convergence. The island model, a common structured population method, divides the population into smaller independent sub-populations called islands, each running in parallel. Migration, the primary technique for promoting population diversity, facilitates the exchange of relevant and useful information between islands during iterations. This paper introduces an enhanced variant of CSA, called Enhanced CSA (ECSA), which incorporates the cooperative island model (iECSA) to improve its search capabilities and avoid premature convergence. The proposed iECSA incorporates two enhancements to CSA. Firstly, an adaptive tournament-based selection mechanism is employed to choose the guided solution. Secondly, the basic random movement in CSA is replaced with a modified operator to enhance exploration. The performance of iECSA is evaluated on 53 real-valued mathematical problems, including 23 classical benchmark functions and 30 IEEE-CEC2014 benchmark functions. A sensitivity analysis of key iECSA parameters is conducted to understand their impact on convergence and diversity. The efficacy of iECSA is validated by conducting an extensive evaluation against a comprehensive set of well-established and recently introduced meta-heuristic algorithms, encompassing a total of seventeen different algorithms. Significant differences among these comparative algorithms are established utilizing statistical tests like Wilcoxon’s rank-sum and Friedman’s tests. Experimental results demonstrate that iECSA outperforms the fundamental ECSA algorithm on 82.6% of standard test functions, providing more accurate and reliable outcomes compared to other CSA variants. Furthermore, Extensive experimentation consistently showcases that the iECSA outperforms its comparable algorithms across a diverse set of benchmark functions.

The evaluation of university management performance using the CS-RBM algorithm.

Amidst the ongoing higher education reforms in China, the escalated investments in colleges and universities underscore the need for an effective assessment of their performance to ensure sustainable development. However, traditional evaluation methods have proven time-consuming and labor-intensive. In response, a novel approach called CS-RBM (Crow Search Restricted Boltzmann Machine) prediction algorithm has been proposed for the educational management of these institutions. By integrating the CS algorithm and an enhanced RBM algorithm, this method facilitates the scoring of project performance indicators, bolstered by insights from user evaluation form reports. The comprehensive project performance is ultimately derived from this combination. Comparative analysis with the standard particle swarm optimization algorithm on public data sets demonstrates a remarkable 45.6% reduction in prediction errors and an impressive 34.7% increase in iteration speed using the CS-RBM algorithm. The accuracy of the tested data set surpasses 98%, validating the efficacy of the CS-RBM algorithm in achieving precise predictions and effective assessments. Consequently, this innovative approach exhibits promising potential for expediting and enhancing the performance evaluation of colleges and universities, contributing significantly to their sustainable development.

Application of henry gas solubility optimization algorithm for short-term hydrothermal scheduling considering variable water transportation delay and penstock head loss

This paper deals with the solution of short-term hydrothermal scheduling considering some practical scenario of hydro system like variable water time delay and penstock head loss along with other constraints related to hydro and thermal plant. The problem has been solved using Henry gas solubility optimization algorithm. This algorithm is basically works based on the principle of Henry's law. A test system consists of seven hydro units and eight thermal units have been adopted for study purpose. This test system is mainly inspired from Damodar valley corporation, India. The problem has been solved in MATLAB platform. The simulation results have been compared with symbiotic organism search algorithm, ion motion optimization algorithm, crow search algorithm and sine cosine algorithm. The results suggest that the proposed algorithm outperforms other algorithms in terms of quality solution and computation efficiency.