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Degree reduction of ball Said-Ball surfaces using an enhanced coot optimization algorithm

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2025年第7期48卷 8297-8321页

作者： Hu, Gang Dou, Wanting Du, Bo Xian Univ Technol Dept Appl Math Xian Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China Xian Univ Technol Sch Comp Sci & Engn 5 South Jinhua Rd Xian 710048 Peoples R China

Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (Ecoot for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, Ecoot algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed Ecoot algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the Ecoot algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original coot algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence.

关键词： ball Said-Ball surface center surface coot optimization algorithm degree reduction radius function

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Hybrid of coot optimization algorithm with Genetic algorithm for Sensor Nodes Clustering Using Software Defined Network

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期138卷 1615-1647页

作者： Hanafi, Amir Vafid Ibrahimoglu, Nadir Ghaffari, Ali Arasteh, Bahman Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Papilon Savunma TR-06570 Ankara Turkiye Istinye Univ Fac Engn & Nat Sci Comp Engn Dept Istanbul Turkiye Khazar Univ Dept Comp Sci Baku Azerbaijan

The main issue in wireless sensor networks (WSNs) is the energy consumption of the nodes. Each sensor node communicates directly with other nodes in its transmission range or uses other nodes to forward the message to nodes outside its range. Software-defined networking (SDN) is a good solution for WSNs by separating the control logic from nodes/drivers. The advantage of SDN-WSNs is that SDN has centralized control over the whole network and deployment of network management protocols and applications becomes easy. In this paper, a new clustering model based on SDN-WSNs is proposed that uses the coot optimization algorithm and Genetic algorithm (GA). GA is used in the proposed model to improve coot. coot-GA is embedded in the SDN controller and is responsible for forming clusters with optimal structures. The SDN controller sends commands to the sensor nodes and finds the best clustering and energy consumption mode by repeated operations. The coot-GA model is evaluated by the number of alive nodes, energy consumption, and packet delivery rate. The coot-GA model is evaluated in two scenarios with 100 and 200 nodes. According to the results, with 100 nodes, the coot-GA model has reduced energy consumption by 8.55%, 6.23%, and 3.90% compared to the Sine Cosine algorithm (SCA), Harris Hawks optimization (HHO) and coot. According to the results, with 200 nodes, the coot-GA model has reduced energy consumption by 13.54%, 10.32%, and 4.51% compared to SCA, HHO, and coot.

关键词： Wireless sensor networks Software-defined networking Clustering coot optimization algorithm Genetic algorithm

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A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved coot optimization algorithm

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RENEWABLE ENERGY 2023年第1期204卷 493-506页

作者： Sheng, Wanxing Li, Rui Yan, Tao Tseng, Ming-Lang Lou, Jiale Li, Lingling China Elect Power Res Inst Beijing 100192 Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia UKM Grad Sch Business Bangi 43000 Selangor Malaysia Asia Univ Dept Business Adm Taichung Taiwan Hebei Univ Technol Sch Elect Engn Tianjin 300401 Peoples R China

This study proposes a hybrid dynamic economics emissions dispatch (HDEED) model for a distributed power system containing thermal generating units, wind farms and photovoltaic plants. The construction of novel distributed power system has been a significant means of tackling the energy crisis. (1) the relationship between each generation unit of the power generation system is analyzed, as well as the power balance constraint, transmission loss constraint, output capacity of each generation unit and slope constraint of the power system;and (2) the operating cost objective function is established with the objective of minimizing unit's generation cost, pollutant emission objective function, and the satisfaction weight coefficient. A novel and improved coot optimization algorithm is presented to enhance convergence performance and solution speed in solving the problem by introducing a chaotic initialization strategy. A mutation strategy and an improved chain movement of the model solution are verified. The result shows that for the HDEED problem, the Icoot algorithm reduces the operating cost targets by 1.28%, 6.99% and 7.44% and the pollutant emission targets by 2.98%, 5.46% and 10.88% compared to other algorithms. The developed model provides an effective solution for improving the operational stability, economy and cleanliness of system.

关键词： Hybrid dynamic economics emissions dispatch New energy power generation coot optimization algorithm Multi -objective optimization Distributed power system

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coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator

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HELIYON 2024年第13期10卷 e32661页

作者： Mohamed, Mohamed Jasim Oleiwi, Bashra Kadhim Azar, Ahmad Taher Hameed, Ibrahim A. Univ Technol Baghdad Dept Control & Syst Engn Baghdad Iraq Prince Sultan Univ Coll Comp & Informat Sci Riyadh 11586 Saudi Arabia Prince Sultan Univ Automated Syst & Soft Comp Lab ASSCL Riyadh 11586 Saudi Arabia Benha Univ Fac Comp & Articial Intelligence Banha Egypt Norwegian Univ Sci & Technol Dept ICT & Nat Sci Larsgardsvegen 2 N-6009 Alesund Norway

Robotic manipulators are nonlinear systems, multi-input multi-output, highly coupled and complicated whose performance is negatively impacted by external disturbances and parameter un-certainties. Therefore, the controllers designed for such systems must be capable of managing their complexity. The main aim of this study is to tackle the trajectory tracking issue of the threeLink Rigid Robot Manipulator (3-LRRM) based on designing three control structures using a combi-nation Neural Network (NN) with Proportional, Integral and Derivative (PID) actions named Neural Controller Like PIPD (NN-PIPD) controller, Neural Network plus PID (NN + PID) controller NN + PID controller and Elman Neural Network Like PID (ELNN-PID) controller. The parameters of the proposed controllers are adjusted utilizing the coot optimization algorithm (COOA) in order to reduce the Integral Time Square Error (ITSE). A novel objective function for tuning process to produce a controller with minimum value of the chattering in the control signal is proposed. The performance of the proposed controllers is evaluated in terms of disturbance rejection, model uncertainty, fluctuating initial conditions and reference trajectory tracking. According to the simulation results proved that the suggested NN-PIPD controller outperforms all other proposed controller structures for tracking performance, stability, and robustness. As a result of the com-parison analysis the optimal controller was considered to be an NN-PIPD controller for tracking trajectory, rejecting disturbances, and parameter variation with minimizing ITSE of 0.001777.

关键词： Neural network PID controller 3-Link rigid robotic manipulator Nonlinear system coot optimization algorithm Trajectory tracking

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Frequency Selective Surface Design Using coot optimization algorithm for 5G Applications

Frequency Selective Surface Design Using Coot Optimization A...

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International Workshop on Antenna Technology (IWAT)

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Salucci, Marco Polo, Alessandro Sarigiannidis, Panagiotis Koulouridis, Stavros Goudos, Sotirios K. Aristotle Univ Thessaloniki Sch Phys ELEDIA AUTH Thessaloniki Greece Univ Trento ELEDIA Res Ctr Trento Italy Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece

ISBN: (纸本)9781665494496

The interest in Frequency Selective Surfaces has been raised during the last years, mostly due to the advent of 5G cellular communication networks. As electromagnetic structures with diverse characteristics, FSSs can provide comparative advantages in 5G communication systems. In this paper, we design and optimize an FSS, which acts as an absorber in the 5G NR n78 frequency band. As an optimizer, we utilize a meta-heuristic swarm intelligence algorithm, i.e., the coot optimization algorithm. The selected algorithm performs the optimization method both to the unit cell and the corresponding FSS structure. Computed results demonstrate quite satisfactory performance of the presented FSS design, in terms of the minimum reflection coefficient, the maximum realized gain, and the maximum efficiency making it a promising candidate for 5G applications.

关键词： Frequency Selective Surface 5G application coot optimization algorithm meta-heuristic swarm intelligence optimization method

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DV-Hop Localization algorithm Based on coot optimization algorithm 5

DV-Hop Localization Algorithm Based on Coot Optimization Alg...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Xu, Jiayue Yu, Xiuwu Ye, Shuai Wang, Xun University of South China School of Electrical Engineering Hengyang China University of South China School of Resource Environment and Safety Engineering Hengyang China

ISBN: (纸本)9798350309034

DV-Hop localization algorithm optimized for the coot algorithm is proposed to address the issue of significant positioning errors in the non distance measurement positioning process of wireless sensor networks. First, refinement of the number of hops between nodes using dual communication radius;whereupon, use the minimum mean square error criterion to calculate the average hop distance of beacon nodes, and introduce a normalized weighting factor to optimize the calculation of the average hop distance;finally, the node coordinates are computed using the coot optimization algorithm instead of the least squares estimation. The simulation results show that under different beacon node densities, the localisation accuracy is improved by 36.8%, 21.9% and 17.6% respectively by the algorithm proposed in this paper, compared to the existing WSN optimisation algorithms, and positioning accuracy was improved by 35.8%, 21.3% and 19.4%, respectively, at different communication radii. © 2023 IEEE.

关键词： coot optimization algorithm dual communication radius DV-Hop localization algorithm wireless sensor network

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Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

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ENERGY 2024年 305卷

作者： He, Xingyue He, Bitao Qin, Tao Lin, Chuan Yang, Jing Guizhou Univ Elect Engn Coll Guiyang 550025 Peoples R China China Power Construction Grp Guizhou Engn Co Ltd Guiyang 550001 Peoples R China Guizhou Univ Comp & Technol Coll Guiyang 550025 Peoples R China

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.

关键词： Wind speed forecasting coot optimization algorithm Long short-term memory network Temporal convolutional network Self-attention mechanism

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Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年第Pt A期151卷 106239-106239页

作者： Hu, Gang Zhong, Jingyu Wang, Xupeng Wei, Guo Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Xian Univ Technol Sch Art & Design Xian 710054 Peoples R China Univ North Carolina Pembroke Pembroke NC 28372 USA

Real-world optimization problems require some advanced metaheuristic algorithms, which functionally sustain a variety of solutions and technically explore the tracking space to find the global optimal solution or optimizer. One such algorithm is the newly developed coot algorithm that is used to solve complex optimization problems. However, like other swarm intelligence algorithms, the coot algorithm also faces the issues of low diversity, slow iteration speed, and stagnation in local optimization. In order to ameliorate these deficiencies, an improved population-initialized coot algorithm named COBHcoot is developed by integrating chaos map, opposition -based learning strategy and hunting strategy, which are used to accelerate the global convergence speed and boost the exploration efficiency and solution quality of the algorithm. To validate the dominance of the proposed COBHcoot, it is compared with the original coot algorithm and the well-known natural heuristic optimization algorithm on the recognized CEC2017 and CEC2019 benchmark suites, respectively. For the 29 CEC2017 problems, COBHcoot performed the best in 15 (51.72%, 30-Dim), 14 (48.28%, 50-Dim) and 11 (37.93%, 100 -Dim) respectively, and for the 10 CEC2019 benchmark functions, COBHcoot performed the best in 7 of them. Furthermore, the practicability and potential of COBHcoot are also highlighted by solving two engineering optimization problems and four truss structure optimization problems. Eventually, to examine the validity and performance of COBHcoot for medical feature selection, eight medical datasets are used as benchmarks to compare with other superior methods in terms of average accuracy and number of features. Particularly, COBHcoot is applied to the feature selection of cervical cancer behavior risk dataset. The findings testified that COBHcoot achieves better accuracy with a minimal number of features compared with the comparison methods.

关键词： coot optimization algorithm Engineering optimization Chaotic map Hunting strategy Opposition -based learning Benchmark Cervical cancer behavior risk

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Harnessing variable reduction approach with deep recurrent neural network for student's academic performance analysis

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ALEXANDRIA ENGINEERING JOURNAL 2025年 118卷 393-405页

作者： Katib, Iyad Ragab, Mahmoud King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

Predicting student performance aids the educational stakeholders in making interventions and taking proactive decisions for developing education quality and addressing the ever-evolving demands of society. Deep learning (DL) methods, particularly recurrent neural networks (RNN) and convolutional neural networks (CNN) have shown great potential in predicting student performance based on different factors like study habits, attendance, and past academic records. These models can learn complex patterns and relationships by processing large datasets, enabling accurate prediction of future educational outcomes. This predictive ability could help educators intervene early and offer targeted support for the student at risk of underperforming, eventually enhancing the overall academic success rate. Simultaneously, the feature selection (FS) for predicting student performance assists in establishing strategic plans for the educational performance enhancement of students and plays a vital role in improving predictive accuracy. There are many FS techniques for predicting student performance, but the study shows several merits and demerits of existing FS techniques in selecting optimum features. This article develops a new variable reduction with an optimal deep recurrent neural network (VR-ODRNN) approach for student performance and course outcome analysis. The VR-ODRNN model aims to examine the student data to identify course grades obtained by the students. Primarily, min-max normalization is used to scale the input data uniformly. For variable reduction, the VR-ODRNN model utilizes a coot optimization algorithm (COA) to choose an optimal subset of features. Besides, the student's performance is predicted by using the DRNN. Finally, the hyperparameter selection of the DRNN approach is performed by employing the Dwarf Mongoose Gannet optimization algorithm (DMGOA). The simulation analysis of the VR-ODRNN model is examined using the student performance dataset from the Kaggle

关键词： Educational Data Mining coot optimization algorithm Feature Selection Student Performance Hyperparameter Selection

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Developing a machine learning-based model using optimization algorithms to estimate the released synthetic gas and solid yield in the gasification process

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第6期8卷 1-20页

作者： Wang, Xiaohe Changchun Coll Elect Technol Dept Gen Educ Changchun 130114 Jilin Peoples R China

Gasification is an important procedure because it efficiently transforms a variety of carbon-containing substances like coal, biomass, and waste into valuable products such as syngas, hydrogen, and synthetic fuels. The effectiveness of the gasification procedure relies on important factors that need to be taken into account. Thus, in the present study, a machine learning (ML) technique was used based on initial data from 312 experiments in order to predict the production rate of raw gases and solids in the gasification process. Over the modeling stage, as the novelty of the research, metaheuristic optimization algorithms as successors were coupled to the Decision Tree (DT) as the main model to tune the performance of the model, leading to simulate gasification efficiently compared to the single model. Raw gases and solids generated at the end of gasification depend on various factors, including input materials and thermodynamic parameters, e.g., temperature. By applying the developed hybrid models with optimizers coot optimization algorithm (COA) and Gradient-based optimizer (GBO), it becomes feasible to create a framework quantifying the influence of the variables on the production of gases and solids. Several assessing criteria were employed to evaluate the performance of developed models. By investigating the results of modeling, hybrid models were found at a higher level of modeling in terms of coefficient of determination with the average calculated R2 of around 98%, while for a single model of 96%. Generally, the developing approach of the bare single AI-based models can be used to simulate thermodynamic operations to give experts more understanding of processes working efficiently.

关键词： Gasification process Biomass Energy production Biochar yield Decision tree coot optimization algorithm Gradient-based optimizer

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