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19th International Conference on Computational Science and Its Applications (ICCSA)

作者： Valdivia, Sergio Crawford, Broderick Soto, Ricardo Lemus-Romani, Jose Astorga, Gino Misra, Sanjay Salas-Fernandez, Agustin Rubio, Jose-Miguel Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Covenant Univ Ota Nigeria Univ Tecnol Chile INACAP Santiago Chile

ISBN: (纸本)9783030243081

The bridges reinforcement is an expensive activity. In this line, new cheap ways to fix bridges have been developed. One of them is the bridges reinforcement through conversion of the cable-stayed arch. The main aim is minimizing the distances between the tensions of each lengthwise position between the original bridge and the bridge with the reinforcement by the cable-stayed arch. For that, it is necessary to know the order that the tension must be applied. In this paper, we resolve this problem by using a recent metaheuristic called crow search algorithm which is based on the intelligent behavior of crows, this approach is inspired by the feature of crows to hide their excess food. The obtained results are compared other approximate techniques by using a well-known statistical test. Promising results reveal that this new algorithm is competitive to solve the proposed problem.

关键词： crow search algorithm Metaheuristic optimization Bridges reinforcement

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Heart Disease Classification using crow search algorithm 5

Heart Disease Classification using Crow Search Algorithm

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Maragatharajan, M. Naveen, P. Nirmala, E. Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

This work introduces cutting-edge research to predict heart diseases. This application designed for accurate and personalized assessment of cardiovascular health. This work aims to provide an overall understanding of an individual's heart health by integrating these multifaceted data points. This work utilizes a vast dataset to establish correlations and patterns, enabling it to categorize users into specific risk groups or diagnose potential heart diseases. Through continuous learning and adaptation, the work refines its predictive capabilities, ensuring that it remains up-to-date with emerging medical knowledge. The work not only assists in early detection of cardiovascular issues but also aids in personalized risk assessment, guiding users towards lifestyle modifications and preventive measures based on their unique health profiles. This innovative solution holds the potential to revolutionize the field of cardiovascular health assessment, offering a proactive and data-driven approach to identify and manage heart diseases efficiently. This study considered two well-known machine learning models namely Extreme learning machine algorithms (ELM) and crow search algorithm (CSA). The collected dataset consists of 10000 samples. We have attained 92.5% accuracy with the data set. The incorporation of machine learning models ensures scalability, adaptability, and precision, making it a valuable tool for both healthcare professionals and individuals striving for proactive heart care. © 2024 IEEE.

关键词： Accuracy crow search algorithm Data preprocessing Extreme learning machine algorithm Feature selection Heart diseases

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Fast Channel Selection Method Using crow search algorithm 19

Fast Channel Selection Method Using Crow Search Algorithm

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International Conference of Information and Communication Technology (ICICT)

作者： Alhakeem, Zaineb M. Ali, Ramzy S. Univ Basrah Dept Elect Engn Basra Iraq

ISBN: (纸本)9781450366434

In Brain Computer Interface (BCI), the brain signals are used to perform some commands or actions in a computer. Brain signals are recorded using many methods. Electroencephalogram (EEG) is one of the non-invasive methods. EEG signals are recorded using multiple channels. Selection methods are used to choose the most relevant and powerful signals. Usually Meta-heuristic algorithms are used for selection. Meta-heuristic algorithms depends on random generated population of solutions for the objective function. Because of the randomness, there is always a chance to select zero as a solution. Zero in EEG channels selection means no channel is chosen to extract its signal features. This situation is not practical, the selection process should be repeated whenever a zero solution appears. The repetition will reduce the algorithm speed. This paper introduces a fast channel selection algorithm using crow search algorithm (CSA). CSA is used to select the best channels offline. Using no-zero channel condition to fasten the algorithm. Our results show that CSA with no-zero channels condition is better than Genetic algorithm (GA). Although CSA and GA results are almost have the same accuracy, but CSA with no-zero condition is faster.

关键词： Brain Computer Interface crow search algorithm Electroencephalography EEG Fast selection Genetic algorithm Motor Imagery

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Exploiting the crow search algorithm to Overcome the Bandpass Problem 2nd

Exploiting the Crow Search Algorithm to Overcome the Bandpas...

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2nd International Conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Al-Sabaawi, Ali M. Ahmed Hussein, Mohsin Hasan Al Gburi, Hussien Qahtan Marhoon, Hayder Abdulameer Nineveh Univ Fac Informat Technol Software Dept Mosul Iraq Gifted Student Sch Nineveh Minist Educ Mosul Iraq Univ Kerbala Fac Comp Sci & Informat Technol Karbala Iraq Minist Educ Adm Affairs IDs Baghdad Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Thi Qar Iraq

ISBN: (纸本)9783031628801;9783031628818

In telecommunication networks, fiber optics is used to transfer data from a source to a destination. One of the common problems in the transmission process is called the Bandpass Problem (BP). BP concentrates on establishing a model that can transfer information on various wavelengths at a minimum cost usingwavelength division multiplexing technology. The data is organized in packets involving various columns. The minimum cost can be obtained by finding the best row permutation in terms of cost in an acceptable time. Sundry studies have been exploited to find the minimum cost at an appropriate time. Although previous studies have reduced the cost and decreased the execution time, they have not reached optimality. Therefore, in this article, a mining technique using a meta-heuristic method called the crow search algorithm (CSA) was applied to achieve the aforementioned goal. The proposed method can find the global minimum cost by keeping the positions of the best row permutation in an acceptable time. The row permutation remains unchanged unless a new better row permutation is computed. The findings exhibited a great deal of insights into how the CSA method outperformed the genetic algorithm, simulated annealing, and the ant bee colony in most cases.

关键词： Bandpass problem crow search algorithm Metaheuristic algorithm

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Global Best Guided Binary crow search algorithm for Feature Selection

Global Best Guided Binary Crow Search Algorithm for Feature ...

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International Conference on Distributed Computing and Optimization Tech-niques (ICDCOT)

作者： Agarwal, Unnati Sahu, Tirath Prasad Natl Inst Technol Raipur Dept Informat Technol Raipur India

ISBN: (纸本)9789811922817;9789811922800

Feature selection is a universal combinatorial optimization problem that is used to enhance the characteristics of high-dimensional datasets by eliminating redundant data and selecting prominent features to generate acceptable classification performance. In optimization problems, the objective function can have several local optima, but the ultimate aim is to identify global optima or values close to global optimum. The crow search algorithm (CSA) is a recently suggested metaheuristic algorithm, implemented to feature selection issues systematically. It is witnessed that the CSA solution search equation is suitable in exploration but poor in exploitation. In this paper, a global best-guided solution to CSA (G-CSA) is proposed and applied to pick the optimum feature subset in a wrapper mode to boost exploitation for classification purposes. The efficiency of the proposed methodology is examined on twelve standard UCI datasets. When comparing experimental results, it is clear that the proposed algorithm is superior to its challengers.

关键词： Classification crow search algorithm Feature selection Optimization

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crow search algorithm-Optimized Cascade Controller in a Multi-Area Thermal Wind Integrated System and Its Real-Time Validation

ELECTRICA

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ELECTRICA 2023年第3期23卷 429-+页

作者： Babu, Naladi Ram Chiranjeevi, Tirumalasetty Saha, Arindita Bhagat, Sanjeev Kumar Gupta, Umesh Kumar Vais, Ram Ishwar Aditya Engn Coll Dept Elect & Elect Engn Surampalem Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinda Andhra Pradesh India Rajkiya Engn Coll Sonbhadra Dept Elect Engn Markundi UP India Regent Educ & Res Fdn Grp Inst Dept Elect Engn Kolkata India NIT Silchar Dept Elect Engn Silchar Assam India

This article explores the load frequency control (LFC) problem for manifold areas and sources under conventional situations. An attempt has been made to demonstrate the solicitation of a real-time simulation laboratory for the LFC studies of three thermal area systems. Thermal systems are integrated with renewable like wind systems in area-1, 2 respectively. A novel ancillary controller termed by cascade tilt integral-proportional integral derivative (TI-PID) is suggested and is augmented by crow search algorithm. The superiority of TI-PID controller is tested and found to be better with PID and tilt integral derivative (TID) controller. Moreover, the obtained responses with hybrid peak area-integral square error are compared with ISE, and it shows better responses over ISE. Further, the responses with alternating current-high-voltage direct- current system improve system dynamics over wind-thermal and thermal systems. Furthermore, sensitivity analysis suggests that the TID with filter controller considerations attained at nominal circumstances are vigorous.

关键词： Load frequency control cascade controller hybrid peak area-integral square error crow search algorithm wind system

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A Novel Technique Based on crow search algorithm to Solve Optimal Control Problem of a Single-Link Rigid Robotic Manipulator 2

A Novel Technique Based on Crow Search Algorithm to Solve Op...

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2nd International Conference on Power Electronics and Energy, ICPEE 2023

作者： Sahu, Venkata Satya Durga Manohar Samal, Padarbinda Panigrahi, Chinmoy Kumar School of Electrical Engineering Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India

ISBN: (纸本)9781665470582

This study shows how Meta-heuristic optimization programming may be implemented to solve constrained optimal control problems. With a single-link rigid manipulator (SLM) model as an example, the proposed crow search algorithm (CSA) is contrasted with the differential evolution algorithm (DE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) in terms of performance index, which has demonstrated the notable outcomes on another algorithm. © 2023 IEEE.

关键词： crow search algorithm optimal control problem single-link rigid manipulator

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Train the Multi-Layer Perceptrons Based on crow search algorithm 1

Train the Multi-Layer Perceptrons Based on Crow Search Algor...

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1st International Conference Information Technology to Enhance E-Learning and other Application, IT-ELA 2020

作者： Khamees, Muntadher Ahmed, Wasan S. Abbas, Saad Q. Diyala University Dept. Computer Sciences Diyala Iraq Bilad Al Rafidain University College Dept. Medical Instruments Engineering Techniques Diyala Iraq

ISBN: (纸本)9781728182339

This paper presents a new approach for training the Multi-Layer Perceptron (MLP) based on the crow search algorithm (CSA) optimization algorithm. The main objective of this approach is to reduce the error to its minimum level and increase the rate of the classification. The benchmark of the proposed approach implementation achieved using more than one standard datasets for classification to ensure the quality of the result, and the performance also compared with other optimization algorithms such as Particle Swarm optimization (PSO), Genetic algorithm (GA), and Ant Colony optimization (ACO). The outcomes indicated that the crow search algorithm was the best since it produces the highest accuracy rate and solves the optimization problem efficiently. © 2020 IEEE.

关键词： crow search algorithm Multi-Layer Perceptron optimization problem

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Computing the Load Margin of Power Systems Using crow search algorithm 7th

Computing the Load Margin of Power Systems Using Crow Search...

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7th International Conference on Advances in Control and Optimization of Dynamical Systems, ACODS 2022

作者： Bento, Murilo E.C. Federal University of Rio de Janeiro RJ Rio de Janeiro21941909 Brazil

Power system operation centers require knowledge of the load level where the system may present instability characteristics. It is common practice to calculate the load margin where the system can collapse in voltage respecting a direction of load growth. However, the increase in load can also cause low damped oscillation modes to appear and cause small-signal stability problems of power systems. This research proposes a crow search algorithm-based method to calculate the load margin of power systems considering small-signal stability and voltage stability requirements. To solve the problem of good initial conditions for the search problem, the Sine Cosine algorithm was used. The proposed method was evaluated in the IEEE 39-bus system and the results achieved show the effectiveness of the method in determining the load margins quickly. © 2022 Elsevier B.V.. All rights reserved.

关键词： crow search algorithm Dynamic Security Assessment Power System Stability Sine Cosine algorithm Small-Signal Stability Smart Grid Voltage Stability

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A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 197卷

作者： Ramachandran, Murugan Mirjalili, Seyedali Ramalingam, Mohan Malli Gnanakkan, Christober Asir Rajan Charles Parvathysankar, Deiva Sundari Sundaram, Arunachalam KCG Coll Technol Dept Elect & Elect Engn Chennai 600097 Tamil Nadu India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld Australia Meenakshi Coll Engn Dept Elect & Elect Engn Chennai 600078 Tamil Nadu India Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India Jubail Ind Coll Dept Elect & Elect Engn Technol Al Jubail 31961 Jubail Ind City 31961 Saudi Arabia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking-based crow search algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking crow search algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual's best solution (p(best)) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

关键词： Combined heat and power economic dispatch crow search algorithm Artificial bee colony algorithm Adaptive ranking selection Fuzzy logic Sine-Cosine

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