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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhao Lingling Chen Quan Zhang Huaying Xie Fang Anhui Univ Sch Elect Engn & Automat Hefei Peoples R China Shenzhen Power Supply Co Ltd New Smart City High Qual Power Supply Joint Lab China Southern Power Grid Shenzhen Peoples R China

ISBN: (纸本)9781665422482

In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis model based on the Strongest crow search algorithm(SCSA) to optimize BP neural network is proposed. In the SCSA-BP fault diagnosis model, adaptive weights are introduced to coordinate the global and local search capabilities of the crow search algorithm(CSA). The crazy factor is introduced to increase the diversity of the crow population and prevent individual crows from falling into the local optimum. Bidirectional Random Optimization(BRO) is added to reduce the amount of nonessential exploration by the crow and to improve the search efficiency of the crow. SCSA is used to optimize the weights and thresholds of the BP neural network to avoid the premature problem of the BP neural network Simulation calculations show that the accuracy of the transformer fault diagnosis of the SCSA-BP model is 8.955% and 4.478% higher than that of the PSO-BP and CSA-BP diagnosis models, respectively.

关键词： transformer fault diagnosis crow search algorithm adaptive weight crazy factor bidirectional random search

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Prediction of Lung Cancer using Meta-Heuristic based Optimization Technique: crow search Technique

Prediction of Lung Cancer using Meta-Heuristic based Optimiz...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Alagarsamy, Saravanan Subramanian, R. Raja Shree, Theepika Kannan, Soundarya Balasubramanian, Mounika Govindaraj, Vishnuvarthanan Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil Tamil Nadu India Kalasalingam Acad Res & Educ Dept Bio Med & Engn Krishnankoil Tamil Nadu India

ISBN: (纸本)9781728185293

In this paper, a meta-heuristic-based optimization technique is used for predicting cancer in the lungs. Lung cancer is one of the dreadful diseases that occurred in human beings and cause a lot of damage. Even though, lots of techniques have been proposed to predict lung cancer. But, still, the prediction of cancer has become a challenging task, due to the multifaceted structures in the CT scan. The automated meta-heuristic-based optimization technique namely crow search algorithm is used to find the feasible position and then searching the similar pixels for the clustering process. The exact position of the tumor is finding out using the crow search algorithm and can be better visualized. This process helps the radiologist to find the tumor in the beginning stage and acts as an efficient method for the prediction process. The sensitivity values (99.12 %) produced by the crow search technique is promising than the conventional techniques.

关键词： Computer tomography crow search algorithm Meta-heuristic algorithm

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Optimizing Resource Utilization in IoT based Smart Home Automation: A Hybrid crow search and Border Collie Optimization Approach

Optimizing Resource Utilization in IoT based Smart Home Auto...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Dhivya, V.P. Adhiyaman, S.V. Elangavi, S. Gokul, R. Department of Information Technology Tiruchengode637 215 India

ISBN: (纸本)9798350359688

Smart home automation systems are now essential parts of contemporary living, providing convenience, energy efficiency, and improved security. Efficient compute offloading solutions are crucial for achieving optimal performance and resource utilisation in these systems. This research presents a new method for improving the efficiency of resource usage in smart home automation. The method utilises a hybrid algorithm that combines the crow search algorithm (CSA) with the Border Collie Optimisation algorithm (BCOA). The hybrid method is designed to tackle the difficulties of computing offloading by combining the advantageous aspects of the collective foraging behaviour of crows and the intellect of border collies. We introduce the hybrid algorithm and describe its design, implementation, and assessment through simulation-based experiments. The results indicate that the proposed approach effectively optimises resource utilisation, enhances system efficiency, and improves overall performance in smart home automation scenarios. This research enhances the field of smart home automation by proposing a new optimisation technique specifically designed to meet the specific needs and limitations of these systems. © 2024 IEEE.

关键词： Border Collie Optimization algorithm Computation Offloading crow search algorithm Hybrid Optimization algorithm Resource Utilization

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Analyzing grid connected shaded photovoltaic systems with steady state stability and crow search MPPT control

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FRONTIERS IN ENERGY REsearch 2024年 12卷

作者： Ben Smida, Mouna Azar, Ahmad Taher Sakly, Anis Hameed, Ibrahim A. Univ Monastir Natl Engn Sch Monastir Tunis Tunisia Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Prince Sultan Univ Automated Syst & Soft Comp Lab ASSCL Riyadh Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt Norwegian Univ Sci & Technol Dept ICT & Nat Sci Alesund Norway

The field of research in maximum power point tracking (MPPT) methods is making significant progress with a wide range of techniques, from simple yet inefficient approaches to more complex but effective ones. Therefore, it is important to suggest a simple and effective strategy to control the global maximum power point (GMPP) of a photovoltaic (PV) system especially under partial shading conditions (PSC). This paper proposes a novel metaheuristic MPPT called the crow search algorithm (CSA) to ameliorate the tracking performance of a grid connected shaded PV system. The CSA is a nature inspired method based on the intelligent behaviors of crows in its search process for hidden food sources. This novel method succeeds to mitigate the adverse impacts of partial shading on the performance of PV systems by accurately tracking the GMPP. Based on the small-signal dynamic model, the stability of the proposed system is analyzed. Simulation results for three different levels of partial shading, including zero, weak, and severe shading, demonstrate the better performance of the suggested CSA compared to fuzzy logic controller (FLC) and Inc-Cond techniques. In fact, the comparison is carried out in terms of simplicity of implementation, high efficiency, and low power loss, decreasing considerably the convergence time.

关键词： global maximum power point tracking photovoltaic system crow search algorithm partial shading conditions small signal stability

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Cloud resource mapping through crow search inspired metaheuristic load balancing technique

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 93卷

作者： Singh, Harvinder Tyagi, Sanjay Kumar, Pardeep Univ Petr & Energy Studies Sch Comp Sci Dept Virtualizat Dehra Dun Uttarakhand India Kurukshetra Univ Dept Comp Sci & Applicat Kurukshetra Haryana India

In the modern era of digitalization, many applications exist that consume a huge computational processing power. These applications due to massive costs involved in procurement and maintenance, looks at resource abundant cloud environments as effective solution. However, cloud environments often suffer from challenges that threaten the performance due to inefficient resource utilization. Load balancing across multiple virtual machines in cloud deployment is one of the major issue that leads to under-utilization of cloud resources. In this research work, the problem of task to resource mapping is addressed using crow search based load balancing algorithm for greater optimization as solution. The solution uses parameters such as average power consumption of data center, average cost of the data center and data center loading for allocating the best resource to the submitted tasks. The proposed solution is validated against standard ant colony optimization based load balancing algorithm. It has been found that the proposed algorithm has outperformed standard algorithm and emerges out as a most optimal load balancing algorithm.

关键词： Cloud computing Data center Virtual machine Ant colony optimization crow search algorithm

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A Modified Niching crow search Approach to Well Placement Optimization

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ENERGIES 2021年第4期14卷

作者： Islam, Jahedul Rahaman, Md Shokor A. Vasant, Pandian M. Negash, Berihun Mamo Hoqe, Ahshanul Khalifa Alhitmi, Hitmi Watada, Junzo Univ Teknol PETRONAS Fundamental & Appl Sci Dept Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Inst Hydrocarbon Recovery Shale Gas Res Grp Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Dept Petr Engn Seri Iskandar 32610 Perak Malaysia Chittagong Univ Engn & Technol Elect & Elect Engn Dept Chittagong 4349 Bangladesh Qatar Univ Coll Business & Econ Doha 00974 Qatar Univ Teknol Petronas Dept Comp & Informat Sci Seri Iskandar 32610 Perak Malaysia

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational search algorithm (GSA), and the crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

关键词： non-convex crow search algorithm well placement optimization multimodal optimization niching crow search algorithm

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Node Localization Method in Wireless Sensor Networks Using Combined crow search and the Weighted Centroid Method

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SENSORS 2024年第15期24卷 4791页

作者： Sankaranarayanan, Suresh Vijayakumar, Rajaram Swaminathan, Srividhya Almarri, Badar Lorenz, Pascal Rodrigues, Joel J. P. C. King Faisal Univ Dept Comp Sci POB 400 Al Hufuf Saudi Arabia SRM Inst Sci & Technol Sch Comp Dept Networking & Commun Chennai 603203 India SRM Inst Sci & Technol Sch Comp Fac Engn & Technol Dept Data Sci & Business Syst Chennai 603203 India Univ Haute Alsace Inst Rech Informat Math Math Automat & Signal F-68100 Colmar France Amazonas State Univ Higher Sch Technol BR-69050020 Manaus AM Brazil

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algor

关键词： localization anchor nodes crow search algorithm range free

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Wrapper-Based Feature Selection and Optimization-Enabled Hybrid Deep Learning Framework for Stock Market Prediction

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第1期23卷 475-500页

作者： Patil, Pankaj Rambhau Parasar, Deepa Charhate, Shrikant Amity Univ Maharashtra Amity Sch Engn & Technol Dept Comp Sci & Engn Mumbai India Amity Univ Maharashtra Dept Comp Sci & Engn Mumbai India Amity Univ Maharashtra Dept Civil Engn Mumbai India

Stock market is a significant element of economic market. Accurate forecasting of stock market is very helpful to shareholders because future prediction of a stock value will elevate the profits of investors. The data acquired from the stock market is a time-series data, in which the values of the stock prices are inherently varied with respect to time. Due to its complexity nature and nonlinearity characteristics, the prediction of stock market becomes very difficult and still it remains a challenging task. In order to cope up with such limitation, this research proposes an effective strategy called Deep Recurrent Rider LSTM to provide an accurate detection of stock market values. The accurate forecasting of stock market is carried out with two classifiers, namely Rider Deep Long Short-Term Memory (Rider Deep LSTM) and Deep Recurrent Neural Network (Deep RNN). The Rider Deep LSTM is derived by the integration of Rider concept with Deep LSTM, whereas the Deep RNN is trained using the proposed Shuffled crow search Optimization (SCSO). Moreover, the SCSO is derived by the integration of Shuffled Shepherd Optimization (SSO) algorithm and crow search algorithm (CSA). Finally, the predicted output is determined based on the error condition. Furthermore, the proposed Deep Recurrent Rider LSTM achieved the MSE and RMSE of 0.018 and 0.132 that shows higher performance with better accuracy. The stock market prediction using the proposed classification model is accurate and improves the effectiveness of the classification.

关键词： Stock market prediction deep RNN rider deep LSTM crow search algorithm shuffled shepherd optimization algorithm

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Complex-Valued Ordinary Differential Equation Modeling for Time Series Identification

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IEEE ACCESS 2019年 7卷 41033-41042页

作者： Yang, Bin Bao, Wenzheng Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang 277160 Peoples R China Xuzhou Univ Technol Sch Informat & Elect Engn Xuzhou 221018 Jiangsu Peoples R China

Time series identification is one of the key approaches to dealing with time series data and discovering the change rules. Therefore, time series forecasting can be treated as one of the most challenging issues in this field. In order to improve the forecasting performance, we propose a novel time series prediction model based on a complex-valued ordinary differential equation (CVODE) to predict time series. A multi expression programming (MEP) algorithm is utilized to optimize the structure of the CVODE model. So as to achieve the optimal complex-valued coefficients, a novel optimization algorithm based on a complex-valued crow search algorithm (CVCSA) is proposed. The chaotic Mackey-Glass time series, small-time scale traffic measurements, Nasdaq-100 index, and Shanghai stock exchange composite index are utilized to evaluate the performance of our method. The results prove that our proposed method could predict more accurately than state-of-the-art real-valued neural networks and an ordinary differential equation (ODE). The CVCSA has faster convergence speed and stronger optimization ability than the crow search algorithm (CSA) and particle swarm optimization (PSO).

关键词： Complex-valued ordinary differential equation crow search algorithm time series

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Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2019年 102卷 47-54页

作者： Abd Elaziz, Mohamed Elsheikh, Ammar H. Sharshir, Swellam W. Zagazig Univ Fac Sci Dept Math Zagazig Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Kafrelsheikh Univ Fac Engn Mech Engn Dept Kafrelsheikh 33516 Egypt

Despite the increasingly rapid advances in the thermoacoustic field, heat transfer process in thermoacoustic-based heat exchangers has not been fully understood yet. In this study, an improved adaptive neuro-fuzzy inference system (ANFIS) based on the crow search algorithm (CSA) is proposed to predict the oscillatory heat transfer coefficient (OHTC). The frequency of the oscillations and the mean pressure are used as inputs to the proposed algorithm, while OHTC is used as the output. To investigate the performance of the proposed method, ANFIS-CSA is compared with traditional ANFIS and the ANFIS based genetic algorithm (ANFIS-GA). The experimental results show the high ability of the proposed ANFIS-CSA model to learn the nonlinear relationship between the inputs and outputs. In addition, it provides higher performance to predict the OHTC value than the other two models in terms of performance measures. (C) 2019 Elsevier Ltd and IIR. All rights reserved.

关键词： Oscillatory heat transfer coefficient Thermoacoustic heat exchanger Adaptive neuro-fuzzy inference system crow search algorithm

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