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Inversion of cooling parameters for structure concrete surrounding the spiral case based on improved coati optimization algorithm

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APPLIED THERMAL ENGINEERING 2025年 270卷

作者： Xu, Wenjie Wang, Gang Ma, Zhenyue Xian, Liquan Zhang, Dongdong Li, Junjie Dalian Univ Technol Sch Infrastruct Engn Dalian 116024 Liaoning Peoples R China China Water Northeastern Invest Design & Res Co Lt Changchun 130012 Jilin Peoples R China

Since the composite structure of metal spiral case and its surrounding concrete is the core of hydro-power plant and the load-bearing main body supporting large or giant turbine generation unit, it is crucial to ensure its structural safety and reliability. This study proposes an inversion method for cooling parameters based on thermodynamic theory to optimize cooling water pipe design and ensure that concrete structure temperature control complies with engineering standards. By integrating the golden sinusoidal factor and polynomial variation strategy, the coati optimization algorithm (COA) is enhanced, resulting in the proposed GPCOA method. Its accuracy, efficiency, and robustness in high-dimensional optimization problems are validated using the CEC2014 test set and basic functions. The REINF element is employed to simulate the cooling water pipe assembly, and the cooling effect is validated through the monitor value, demonstrating the rationality of the inversion parameters. Based on the inversion results, a water pipe unit is established, and finite element simulation is conducted. The results demonstrate that the temperature of the concrete surrounding the spiral case complies with engineering design standards, the temperature drop rate is effectively controlled, thermal stress from sudden temperature changes is mitigated, and the structure's safety and reliability are ensured. The inversion method proposed in this study, based on energy transfer principles, overcomes the uncertainties of traditional empirical cooling water pipe design, provides a comprehensive quantitative reference for engineering practice, and demonstrates strong versatility and engineering application value.

关键词： Hydropower plant Spiral case and surrounding concrete Water pipe cooling Parametric inversion improved coati optimization algorithm

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Intelligent control method for automatic voltage regulator: An improved coati optimization algorithm-based strategy

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MEASUREMENT 2025年 252卷

作者： Liu, Shuangxi Lin, Zehuai Feng, Ruizhe Huang, Wei Yan, Binbin Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hyperson Technol Lab Changsha 410073 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

The automatic voltage regulator (AVR) is essential for maintaining voltage stability and ensuring the normal operation of the power system, thereby promoting sustainable energy use. This paper proposes a controller for the AVR based on the improved coati optimization algorithm (ICOA). First, the ICOA is developed by incorporating chaotic mapping and Gaussian random walk strategies into the coati optimization algorithm (COA). Second, a PID controller model based on ICOA tuning is established. Finally, a comprehensive simulation analysis is conducted, comparing the tuning results with those of four other evolutionary algorithms (EAs). The results demonstrate that the proposed ICOA accelerates convergence speed, resulting in an overall improvement in optimization performance. This ensures that the controller parameters tuned by ICOA exhibit better effectiveness, superiority, and robustness compared to other EAs. Additionally, an in-depth time-domain and frequency-domain analysis is performed to fundamentally analyze the advantages brought by the tuned parameters to the AVR.

关键词： Automatic voltage regulator improved coati optimization algorithm Chaotic mapping Gaussian random Parameter tuning Intelligent control

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A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm

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CHAOS SOLITONS & FRACTALS 2024年 187卷

作者： Wang, Chao Lin, Hong Yang, Ming Fu, Xiaoling Yuan, Yue Wang, Zewei Xinjiang Univ Sch Elect Engn Urumqi 830047 Xinjiang Peoples R China Changji Univ Sch Energy & Control Engn Changji 831100 Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China HUaneng Xinjiang Energy Dev Co Ltd Energy Branch Urumqi 830000 Xinjiang Peoples R China

Wind power prediction plays a pivotal role in increasing the power grid stability and mitigating market transaction risks. To enhance the prediction accuracy of wind power, this study presents a novel chaotic time series wind power point and interval prediction approach, focusing on data processing, as well as an improved coati optimization algorithm. First, the improved wavelet threshold denoising (IWTD) technique is constructed to eliminate the noise from the original wind power data. Then, the maximal information coefficient (MIC) is adopted to determine the optimal inputs of the prediction model. Subsequently, the improved coati optimization algorithm (ICOA) is developed to optimize the hyperparameters of the bidirectional long short-term memory (BiLSTM), deep belief network (DBN), and gated recurrent unit (GRU) models, along with the linear weight coefficients of the combined model, thereby enhancing the point prediction accuracy. Finally, kernel density estimation (KDE) is employed to obtain interval predictions for wind power with different confidence levels. Results show that the proposed prediction method effectively improves the prediction accuracy compared with other popular prediction models.

关键词： Data denoising improved coati optimization algorithm Interval prediction Combined prediction model

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Quantitative analysis of millet mixtures based on terahertz time-domain spectroscopy and improved coati optimization algorithm

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SPECTROSCOPY LETTERS 2024年第1期57卷 31-44页

作者： Yin, Xianhua Tian, Hao Zhang, Fuqiang Li, An Guilin Univ Elect Technol Sch Elect Engn & Automat Guangxi Key Lab Automat Detecting Technol & Instru Guilin Peoples R China Guilin Inst Testing Prod Qual Guilin 541004 Guangxi Peoples R China

The significant difference in economic and nutritional value between new and old cereals has led to a plethora of grain mix-ups on the market, and it is necessary to distinguish the old and new grains accurately and quickly. In this study, millet, which has high economic and nutritional value in cereals, was used as the research object. Terahertz time domain spectroscopy combined with chemometrics was used to detect its mixture. The experimental findings show that, compared to the partial least squares regression model, and the support vector regression models optimized by particle swarm and grid algorithms, the support vector regression model optimized by the coati optimization algorithm (COA) exhibits superior performance. In order to further improve the prediction accuracy of the support vector regression model, an improved COA was proposed. The quality and diversity of the initial population are improved by introducing chaotic mapping, tent mapping, and opposition based learning mechanisms, followed by the introduction of adaptive weights and Wright flight mechanisms, which enhance the flexibility and convergence speed of the algorithm, and finally, the introduction of the sine-cosine algorithm to improve the search range. The experiments demonstrate that a support vector regression model optimized by an enhanced coati's algorithm provides superior quantitative analysis of millet mixtures as compared to other models tested. The optimized model achieves high prediction accuracy, with correlation coefficients reaching 0.9872 and 0.9742 for millet mixtures with gradient ratios of 10% and 2%, respectively. This research provides technical support and a reference for rapid, nondestructive quantitative detection of millet mixtures while opening up new possibilities for subsequent quantitative detection of other cereals.

关键词： Terahertz time-domain spectroscopy support vector machine millet quantitative analysis improved coati optimization algorithm

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Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2024年第4期12卷 768-780页

作者： Tamilarasu, P. Singaravel, G. Univ Coll Engn Villupuram Dept Informat Technol Villupuram 605103 Tamil Nadu India KSR Coll Engn Autonomous Dept Informat Technol Namakkal 637215 Tamil Nadu India

The task scheduling has a potential impact on overall system performance and resource utilization in the domain of Cloud Computing. Cloud computing adopts a cloud service provider (CSP) for facilitating access and delivering services to the shared resources. Task scheduling in clouds can be attained some symmetry form which helps in achieving predominant resource optimization that includes energy efficiency and load balancing. This task scheduling process of cloud computing pertains to the problem of Non-deterministic Polynomial (NP) which can be significantly solved using the techniques of metaheuristic optimization for enhancing the job scheduling effectiveness. In this paper, an improved coati optimization algorithm-based Task Scheduling (ICOATS) is presented for addressing the issues of lengthier scheduling time, high consumptions of cost and maximized load on Virtual Machine (VM) in cloud computing environment. In this proposed ICOATS, a model for distribution and scheduling of tasks is constructed using the factors of VMs, cost and time. It further included a multiobjective fitness function which targets on minimizing makespan, and at the same time maximizing the rate of resource utilization. It established possible plan for every coati with respect to task scheduling process that aids in determining the best solution (optimal assignment of incoming tasks to VMs). It is proposed with the capability of handing the problem of premature convergence by incorporating an exploitation strategy which improving the local search potential with well-balanced trade-off amid exploration and exploitation. The simulation results of this ICOATS approach under its evaluation with the existing metaheuristic task scheduling approaches confirmed better improvement in reducing makespan, and simultaneously enhances the turnaround efficiency, success rate and availability.

关键词： Cloud computing Task scheduling improved coati optimization algorithm (ICOA) Resource utilization Virtual machines

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An aeration requirements calculating method based on BOD5 soft measurement model using deep learning and improved coati optimization algorithm

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JOURNAL OF WATER PROCESS ENGINEERING 2024年 64卷

作者： Zhao, Wangben Liu, Yuling Zhou, Xing Li, Shuaishuai Zhao, Chenxu Dou, Chuanchuan Shu, Hao Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg Xian Peoples R China

Conventionally, the supply-side is over-aerated to meet the oxygen demand of biological wastewater treatment in wastewater treatment plants (WWTPs). Such systems do not consider the various influent loadings and actual oxygen consumption. It cannot respond effectively to fluctuating influent, resulting in unnecessary aeration and energy wastage. In this study, we developed a novel approach to calculate aeration requirements in real time. Firstly, the oxygen demand formula for biological nitrogen and phosphorus removal is modified by considering realistic factors (temperature, oxygen partial pressure and so on) to construct the aeration demand formula. Then, to address the problem that BOD 5 in aeration formula is difficult to measure in real time, a novel hybrid model integrating Fuch mapping, reverse learning strategy, coati optimization algorithm (COA), and Backpropagation Artificial Neural Network (BP-ANN) is proposed in this study. Finally, aeration requirements are calculated based on real-time water quality data. Experimental results show that the ICOA-BP model with 9 neurons in hidden layer predicted the optimum BOD 5 concentration by achieving a 25.146 mean absolute error and a 0.735 coefficient of determination (all-R 2 ) in all datasets. The optimization led to a 15.164 % aeration savings and 38.942 % energy savings. The proposed method of aeration calculation is automated and intelligent, responding to fluctuating changes in influent quality and reducing unnecessary aeration and energy consumption while meeting the requirements of effluent quality.

关键词： BOD 5 soft measurement improved coati optimization algorithm Aeration requirements Fuch mapping Reverse learning strategy

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Optimal Scheduling of Electric Vehicle Loads Based on Multi-Strategy improved coati optimization algorithm 50

Optimal Scheduling of Electric Vehicle Loads Based on Multi-...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Tian, Libin Li, Hongwei Liu, Hongpeng School of Electrical Engineering Northeast Electric Power University Jilin China

ISBN: (纸本)9781665464543

In order to solve the problem of excessive distribution grid load power caused by the superposition of disorderly charging of electric vehicles (EV) and user base load peaks, we propose to establish a cloud-side-end cooperative optimization scheduling framework based on the theory of cloud-side coordination and big data technology. Firstly, based on the cloud-side collaboration theory and big data technology, a cloud-side-end collaborative optimization scheduling framework is established with comprehensive interconnection and interoperability of distribution grids, charging station operators, smart charging piles and EV users' information;secondly, a charging scheduling mechanism for EV users that takes into account the minimum acceptable profit or maximum cost of the users is proposed;and then a two-layer multi-objective charging/discharging optimization and control model is established from the perspective of the grid-side and the user-side to propose improved coati optimization algorithm (ICOA) with multiple strategies;finally, the model is solved by applying MATLAB using the load data in the commercial area. The simulation results verify the validity and superiority of the proposed model and method. © 2024 IEEE.

关键词： Cloud Edge Cooperative Scheduling Framework Electric Vehicle Scheduling Mechanism Electric Vehicles improved coati optimization algorithm Ordered Charging and Discharging

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Integrating improved coati optimization algorithm and Bidirectional Long Short-Term Memory Network for Advanced Fault Warning in Industrial Systems

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PROCESSES 2024年第3期12卷 479页

作者： Ji, Kaishi Dogani, Azadeh Jin, Nan Zhang, Xuesong 76 Chongmingdao East Rd Qingdao 266000 Peoples R China Ferdowsi Univ Mashhad Dept Agr Econ Fac Agr Mashhad *** Iran

In today's industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but also bolsters equipment reliability and safety. However, the intricacies and non-linearity inherent in industrial data often pose challenges to traditional fault warning methods, resulting in diminished performance, especially with complex datasets. To address this challenge, we introduce a pioneering fault warning approach that integrates an enhanced coati optimization algorithm (ICOA) with a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Our strategy involves a triple approach incorporating chaos mapping, Gaussian walk, and random walk to mitigate the randomness of the initial solution in the conventional coati optimization algorithm (COA). We augment its search capabilities through a dual population strategy, adaptive factors, and a stochastic differential variation strategy. The ICOA is employed for the optimal selection of Bi-LSTM parameters, effectively accomplishing the fault prediction task. Our method harnesses the global search capabilities of the COA and the sophisticated data analysis capabilities of the Bi-LSTM to enhance the accuracy and efficiency of fault warnings. In a practical application to a real-world case of induced draft fan fault warning, our results indicate that our method anticipates faults approximately two hours in advance. Furthermore, in comparison with other advanced methods, namely, the improved Social Engineering Optimizer Optimized Backpropagation Network (ISEO-BP), the Sparrow Particle Swarm Hybrid algorithm Optimized Light Gradient Boosting Machine (SSAPSO-LightGBM), and the improved Butterfly optimization algorithm Optimized Bi-LSTM (MSBOA-Bi-LSTM), our proposed approach exhibits distinct advantages and

关键词： fault warning improved coati optimization algorithm bidirectional long short-term memory industrial data analysis predictive maintenance

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Task Offloading optimization for Multi-objective Based on Cloud-Edge-End Collaboration in Maritime Networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 164卷

作者： Liu, Lingqiang Zhang, Ying Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

In recent years, global maritime activities have surged, yet maritime networks face significant limitations in capacity. To address this challenge, integrating mobile edge computing into maritime networks has emerged as a solution, enabling the offloading of computation-intensive tasks to the edge to enhance system performance. However, existing research often narrowly focuses on either system cost or Quality of Service (QoS), failing to optimize both concurrently. This study aims to bridge this research gap by proposing a novel approach that optimizes both system cost and QoS simultaneously through collaborative computing among terminals, edge servers, and a cloud server in a maritime network environment. We leverage the improved coati optimization algorithm (ICOA) to optimize transmission power for vessel users, and subsequently, we apply Binary Particle Swarm optimization (BPSO) to make task offloading decisions that consider both system cost and QoS. Experimental results demonstrate that our proposed approach significantly outperforms existing benchmark algorithms in balancing system cost and QoS in cloud-edge-end collaborative scenarios.

关键词： maritime networks cloud-edge-end collaboration task offloading improved coati optimization algorithm Binary Particle Swarm optimization

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Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems

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IEEE ACCESS 2024年 12卷 177298-177311页

作者： Alamro, Hayam Mansouri, Wahida Saeedi, Kawther Alshammeri, Menwa Aljabri, Jawhara Abdullah Alotaibi, Faiz Negm, Noha Sharif, Mahir Mohammed Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Northern Border Univ Fac Sci & Arts Dept Comp & Informat Technol Turaif Arar 91431 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Rabigh 25732 Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Univ Tabuk Univ Coll Umluj Dept Comp Sci Tabuk Saudi Arabia King Saud Univ Coll Humanities & Social Sci Dept Informat Sci Riyadh 11437 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha 61421 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16273 Saudi Arabia

Security donates itself as one of the largest attacks on the support and development of the Internet of Things (IoT). Security challenges are evident in cyber-security threads that direct the main Internet service provider and weaken a significant part of the complete Internet by benefiting from defective and vulnerable embedded gadgets. Numerous devices inhabit at-home systems with user-administrators unfamiliar with network security best practices, creating simple goals for the attackers. So, security solutions are required to direct the untrusted and insecure public networks by mechanizing defences over affordable and nearby direct network data sharing. The growth of automatic cyberattack classification and detection tools utilizing artificial intelligence (AI) and machine learning (ML) devices become vital to achieving safety in the IoT environment. It is desired that safety issues allied to IoT devices be effectively diminished. This article proposes an Advanced Ensemble Transfer Learning for Cyberthreat Detection in Low Power Systems (AETL-CDLPS) technique. The primary intention of the AETL-CDLPS technique is to automate the detection of cyber-attacks for IoT-assisted resource-constrained systems. The AETL-CDLPS technique utilizes a linear scaling normalization (LSN) model to normalize the input data. Next, the AETL-CDLPS technique employs an improved coati optimization algorithm (ICOA)-based feature selection technique to choose optimal features. For the cyber threat detection process, an ensemble transfer learning (TL) model comprises three classifiers, namely gated recurrent Unit (GRU), deep convolutional neural network (DCNN), and stacked sparse autoencoder (SSAE). Finally, the Bayesian optimization algorithm (BOA) is utilized to optimize the hyperparameter tuning of the three ensemble techniques. The AETL-CDLPS model's performance validation is performed using the Bot-IoT dataset. The comparison study of the AETL-CDLPS method portrayed superiorAccuracy, P

关键词： Internet of Things Security Cyberattack Feature extraction optimization Accuracy Transfer learning Convolutional neural networks Computational modeling Bayes methods Ensemble transfer learning cyberthreat detection low power systems improved coati optimization algorithm

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