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Journal of Engineering Research (Kuwait) 2024年

作者： Yang, Qirong Wang, Hechun Yang, Chuanlei Wang, Yinyan Hu, Deng Wang, Binbin Duan, Baoyin Harbin Engineering University Harbin 150001 China

For the digitization of turbocharger, the prediction of compressor working state is essential. How to build a model with accurate prediction and less time-consuming is the premise of studying the digitization of turbochargers. As the relationship between compressor parameters is obtained through experiments, it cannot be expressed by simple functional equations, so the surrogate model is often used for fitting the curve. Five surrogate models, the Kriging model, Response Surface Methodology, Artificial Neural Networks, Radial Basis Function, and Support vector machines, were used to fit and regression compressor characteristic curves. And four optimization algorithms, Particle Swarm optimization, Genetic algorithm, Gray Wolf algorithm, and Firefly algorithm, were used to optimize the model. A method to construct a hybrid surrogate model is proposed. The results show that the influencing factors of the modeling pressure ratio and efficiency at all speed groups were confirmed;Different optimization algorithms have different optimization degrees for the five surrogate models;The prediction accuracy of the hybrid surrogate model is better than the optimized model and the single model. The constructed model can be applied in the digital twins system to predict the working state of the compressor in time to achieve the purpose of rapid response. © 2024 The Authors

关键词： Compressor Characteristics Digital twins Machine Learning optimization algorithm Turbocharger

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optimization of Multispacecraft Maneuvers for Mobile Target Tracking from Low Earth Orbit

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第2期60卷 581-590页

作者： Morgan, Sarah J. McGrath, Ciara N. de Weck, Olivier L. Aerosp Corp Performance Modeling & Anal Dept 14745 Lee Rd Chantilly VA 20151 USA Univ Manchester Dept Mech Aerosp & Civil Engn Aerosp Syst Oxford Rd Manchester M13 9PL England MIT Dept Aeronaut & Astronaut Apollo Program Astronaut & Engn Syst 77 Massachusetts Ave Cambridge MA 02139 USA

This paper presents a method of planning spacecraft maneuvers for mobile target tracking. Agile, maneuverable spacecraft have been proposed as a means of modifying satellite orbits to observe discrete targets on the Earth on demand. This work adapts this concept to propose a method of using maneuverable spacecraft to observe a mobile target as it moves across the Earth. Previous work has shown the potential of such an approach to increase persistence of coverage of a moving target. This work applies a suitable optimizer to select possible maneuvers for a spacecraft, or a constellation of spacecraft, to repeatedly observe a moving target. A biased random key genetic algorithm is used, which adjusts the delta V for each maneuver to minimize the total delta V used and maximize target coverage over the full sequence of maneuvers. The developed method is applied to two case studies concerning monitoring of tropical storms. The results indicate that, using relatively small maneuvers, spacecraft orbits can be adjusted to improve the quantity and quality of views of a moving target. In the Typhoon Megi case study, a single spacecraft using less than 2.5 m/s delta V is shown to double the access time and provide two additional observations of the storm eye compared with a nonmaneuvering spacecraft in an identical initial orbit. Opportunities for observations increase as the number of maneuverable spacecraft are increased, with a three-spacecraft constellation able to provide five complete observations of the storm eye for a total change in velocity of 12 m/s.

关键词： Low Thrust Maneuvers Maneuverable Spacecraft Satellite Constellations Genetic algorithm Spacecraft Guidance optimization algorithm

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Genetic algorithm optimization of a horizontal axis wind turbine blade section performance equipped with a single dielectric barrier discharge plasma actuator utilizing a direct regression model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第20期236卷 10456-10469页

作者： Fadaei, Mohaddeseh Davari, Ali Reza Sabetghadam, Fereidoun Islamic Azad Univ Ramsar Branch Dept Mech & Aerosp Engn Ramsar *** Iran Islamic Azad Univ Sci & Res Branch Dept Aerosp Engn Ramsar Iran

The usage of a single dielectric barrier discharge plasma actuator (SDBD-PA) device for improving a aerodynamic performance of a locally developed horizontal axis wind turbine blade section is studied with one of the most recent electrostatic models. To characterize this blade section behavior over a wide range of operating conditions of SDBD-PA and to advance its performance with a fully automated optimization algorithm, a computationally cost efficient direct regression model is proposed. In this paper, 512 comprehensive numerical simulations are carried out to derive the direct regression model for aerodynamic performance calculation when the PA is in use. However, to obtain highly accurate results, two different models for angle of attacks higher and less than 21 degrees are suggested. The proposed mathematical model within the specified boundary limits allows for a rapid linkage between aerodynamic performance and genetic algorithm which canbe made to acquire optimum results for each case without requiring burdensome numerical simulations. It is identified that superimpose input parameters effects onto each other is not explanatory of the final effect on aerodynamic performance and interaction effects should be seriously taken into consideration at the proposed regression model.

关键词： Direct regression model wind turbine plasma actuator optimization algorithm aerodynamic performance

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An improved Harris Hawks optimizer combined with extremal optimization

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第3期14卷 655-682页

作者： Zhang, Hai-Lin Chen, Min-Rong Li, Pei-Shan Huang, Jun-Jie South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Natl Joint Engn Res Ctr Network Securit Detect & Guangzhou 510632 Peoples R China

Harris Hawks optimizer (HHO) is a new swarm intelligence optimization algorithm proposed in recent years. It seeks the optimal solution by simulating the predation strategy of Harris hawks and many previous experiments show that HHO has a good effect on solving optimization problems. However, HHO also has the shortcomings of low convergence accuracy and easy to fall into local optimum. In order to improve the performance of HHO, an improved HHO hybridized with extremal optimization (IHHO-EO) is proposed. Aiming at the defect of insufficient information utilization and excessive randomization in the exploration phase of the algorithm, the own historical optimal position of Harris hawks is introduced to better guide the individuals to search for better positions and improve the global search ability. Secondly, a nonlinear prey energy escaping factor is proposed to better balance the exploration and exploitation phases. Thirdly, refracted opposition-based learning (ROBL) with a dynamic parameter is proposed and combined with HHO, which can improve the quality of solutions and convergence speed. Finally, the exploitation ability is improved by performing EO operation which has strong local search ability. The proposed algorithm is applied to 23 classical benchmark test functions and 29 CEC2017 test functions. IHHO-EO is compared with HHO, other newly proposed optimization algorithms and some improved variants of HHO. The experimental results verify the effectiveness of the added strategies. In addition, the proposed approach is applied to solving the pressure vessel design problem. The results show that IHHO-EO has an excellent performance in terms of accuracy, reliability and statistical tests.

关键词： optimization algorithm Harris Hawks optimizer Refracted opposition-based learning Extremal optimization

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Sustainable optimization of Construction Project Portfolio Management Through a Data-Driven Approach 2nd

Sustainable Optimization of Construction Project Portfolio M...

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2nd Decision Science Alliance International Summer Conference-DSA ISC

作者： Lostumbo, Marisa A. Bara Iniesta, Marc Calvet, Laura Univ Oberta Catalunya Dept Comp Sci Barcelona Spain EAE Business Sch Barcelona Spain Univ Autonoma Barcelona Telecommun & Syst Engn Dept Sabadell Spain

ISBN: (纸本)9783031782374;9783031782381

Effective management of construction project portfolios demands informed decisions driven by data and mathematical models, aiming to enhance decision-making and address complex decision problems. This article introduces an innovative approach that prioritizes sustainability criteria selection, utilizing data-driven insights and mathematical optimization to optimize project portfolio management. Through multi-objective optimization, our study targets improved resource efficiency, time reduction, and minimized environmental impact, offering portfolio managers a powerful tool for decision-making.

关键词： Project Portfolio Management Decision-Making Data-Driven Sustainability optimization algorithm

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Machine-Learning-Assisted optimization of Aircraft Trajectories Under Realistic Constraints

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第9期46卷 1814-1825页

作者： Xu, Yifan Wandelt, Sebastian Sun, Xiaoqian Yang, Yichen Jin, Xianfei Karichery, Sureshan Drwal, Maciej Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Sabre Corp 3150 Sabre Dr Southlake TX 76092 USA CAE Corp 8585 Cote Deliesse St Laurent Quebec City PQ H4T 1G6 Canada

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Importance measure construction and solving algorithm oriented to the cost-constrained reliability optimization model

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期222卷 108406-108406页

作者： Liu, Mingli Wang, Dan Zhao, Jiangbin Si, Shubin Northwestern Polytech Univ Sch Mech Engn Dept Ind Engn Xian 710072 Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Ind Engn & Intelligent Mfg Xian 710072 Peoples R China Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Peoples R China

Importance measures are used to prioritize the system components for achieving high efficiency and economy of reliability optimization. The existing importance measures pay more attention to evaluating the impact of component performance changes on the objective function (such as system reliability, cost and so on). Still, these importance measures do not consider the influence of constraints in the system reliability optimization models during the ranking process of components, which may leave the component with the largest importance measure almost no space to improve its reliability. For the cost-constrained reliability optimization model (ROM), this paper proposes a cost-constrained ROM-based importance measure (CRIM) by comprehensively considering the objective function and constraints, and several properties of CRIM are discussed. Then, the CRIM-based genetic algorithm (CRIGA) is developed to solve the cost-constrained ROM. The numerical experiment for different scale systems is implemented to evaluate the performance of CRIGA by comparing it with other optimization algorithms. Experimental results show that CRIGA can get better solutions with faster convergence speed and better robustness. Finally, the case on the coal transportation system of the thermal power plant is introduced to illustrate the application of CRIM and CRIGA in reliability optimization.

关键词： Importance measure Reliability optimization model Constraint conditions optimization algorithm

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A new two-axis solar tracker based on the online optimization method:Experimental investigation and neural network modeling

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Energy and AI 2023年第4期14卷 341-359页

作者： Amir Pouya Masoumi Vahid Bagherian Ali Reza Tavakolpour-Saleh Elham Masoomi Department of Mechanical and Aerospace Engineering Shiraz University of TechnologyShirazIran Department of Mechanical Engineering Shiraz UniversityShirazIran Department of Computer Engineering Qazvin BranchIslamic Azad UniversityQazvinIran

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement *** this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the *** magnitude of the sunray is considered as the cost function of all ***,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization *** initialization leads to faster convergence compared to random *** results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 *** average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other *** also performs well with a population size of 15 and 7 ***,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from *** of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network *** performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target ***,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.

关键词： Intelligent two-axis solar tracker optimization algorithm Particle swarm optimization algorithm Online optimization method Artificial neural network

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Hyper tuning hybrid MLP-CatBoost classifier using Mayfly optimization for detection of heart disease

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HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2025年 1-23页

作者： MahaLakshmi, N. Venkata Rout, Ranjeet Kumar Natl Inst Technol Hazratbal Srinagar 190006 Jammu & Kashmir India DR BR Ambedkar Natl Inst Technol Jalandhar Dept IT Jalandhar 144001 Punjab India

Diagnosing heart disease is considered a difficult task as it provides a digital estimation of the seriousness of the disease. As a result, the quickest treatment can be done. So, heart diagnosis has attracted a lot more attention in the medical industry throughout the globe, and along with excellence in efficacy, the optimization algorithm plays a crucial role in the detection of heart disease. Here, to predict the cardiac illness an improvised CatBoost algorithm and Multi-layer Perceptron classifier are used. Also, proper hyperparameter tweaking is needed for the successful implementation of the classifier. In order to optimize the hybrid model's hyperparameters, the Mayfly optimization algorithm is deployed for effective hyperparameter optimization. In order to increase prediction accuracy, the Harris-Hawks optimization technique is used to choose the essential features from the dataset. Z-Alizadeh Sani and Cleveland heart disease datasets are utilized to detect heart disease. Also, it is compared with the existing models. To validate the efficiency of a model, six various measures are used: precision, accuracy, recall, the F- 1 measure, specificity, and loss. Here, when compared to the previous studies, the proposed model yields better performance, i.e., 98.7% accuracy with Cleveland and 99.2% with Alizadeh Sani Datasets.

关键词： Machine learning Hyper-parameters tuning optimization algorithm Mayfly optimization algorithm Multi-layer perceptron Classification CatBoost algorithm

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BIM-based steel reinforcing bar detail construction design and picking optimization

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STRUCTURES 2023年 49卷 520-536页

作者： Lien, Li-Chuan Dolgorsuren, Unurjargal Chung Yuan Christian Univ Dept Civil Engn Taoyuan 320 Taiwan

Steel reinforcing bar (SRB) engineering is a very professional, complex, massive, and neglected project, which affects the structural performance and cost of building. Traditional SRB engineering is a labor-intensive project, often accompanied by the quality of SRB construction and the waste of material. At present, information tech-nology is undergoing a revolution. With the rise and optimization of Building Information Modeling (BIM) technology and the continuous improvement of artificial intelligence-based optimization algorithms, new solu-tions have been put forward to solve these traditional SRB engineering problems. This study combines two different fields of BIM and optimization algorithms to establish a process of BIM-based SRB detail construction design and picking optimization that provides a reference for traditional SRB engineering. It is also an explo-ration and application of BIM technology to practical engineering. This study classified and analyzed the SRB crash nodes problems classified into four crash nodes classification and proposed a BIM-based SRB designing process. For the problem of the SRB connection position, a general segmental formula for SRB (for example, Longitudinal SRB on columns and beams) was studied, and a set of processes was established to determine the unknowns of the segment formula to locate the connection position of SRB. Besides, this study utilizes BIM's coherent information transfer and visualization technologies that select Revit software to create new information such as "SRB shape family," "component family," "annotated family," and so on. The application of BIM in SRB is carried out through the transfer, transformation, and expression of information in the forms of SRB design drawings, SRB processing schedules, SRB construction schedules, and SRB construction partition charts, and so on. in addition, the problem of one-dimensional blanking of single-size and multiple-size raw materials is discussed with the minimum numb

关键词： Building information modeling Detail construction design Picking optimization optimization algorithm Particle Bee algorithm

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