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Design optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model

algorithmS 2023年第8期16卷 380-380页

作者： Nourian, Navid El-Badry, Mamdouh Jamshidi, Maziar Univ Calgary Dept Civil Engn Calgary AB T2N 1N4 Canada

One of the primary objectives of truss structure design optimization is to minimize the total weight by determining the optimal sizes of the truss members while ensuring structural stability and integrity against external loads. Trusses consist of pin joints connected by straight members, analogous to vertices and edges in a mathematical graph. This characteristic motivates the idea of representing truss joints and members as graph vertices and edges. In this study, a Graph Neural Network (GNN) is employed to exploit the benefits of graph representation and develop a GNN-based surrogate model integrated with a particle swarm optimization (PSO) algorithm to approximate nodal displacements of trusses during the design optimization process. This approach enables the determination of the optimal cross-sectional areas of the truss members with fewer finite element model (FEM) analyses. The validity and effectiveness of the GNN-based optimization technique are assessed by comparing its results with those of a conventional FEM-based design optimization of three truss structures: a 10-bar planar truss, a 72-bar space truss, and a 200-bar planar truss. The results demonstrate the superiority of the GNN-based optimization, which can achieve the optimal solutions without violating constraints and at a faster rate, particularly for complex truss structures like the 200-bar planar truss problem.

关键词： artificial neural network design optimization graph neural network particle swarm optimization algorithm size optimization surrogate model truss structures

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Research on High Proportion Renewable Energy of Hybrid Energy Storage Containing Hydrogen Storage 47

Research on High Proportion Renewable Energy of Hybrid Energ...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Wang, Ying Li, Yong Gang Sun, Xiuting Zhao, Shumin Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781665435543

Based on whether the hybrid energy storage system with hydrogen storage can well adapt to the problem of high permeability operation of renewable energy, this paper designs the hybrid energy storage configuration method with electric hydrogen coupling. Firstly, the structure of hybrid energy storage multi-energy complementary power generation system is established, followed by the objective function, which combines the cost, expected energy not supplied, and energy surplus rate of the energy storage system. Then, the particle swarm optimization algorithm is used to solve the optimization problem. Finally, by comparing with the traditional single battery energy storage system, it is concluded that the hybrid energy storage technology can well adapt to the operation of high permeability, not only can well reduce the rate of renewable energy abandonment, but also can effectively reduce the expected energy not supplied and cost.

关键词： hydrogen energy storage hybrid energy storage system high permeability operation abandoned renewable energy rate particle swarm optimization algorithm

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Multi objective optimization model of demand side resource aggregation in market environment

Multi objective optimization model of demand side resource a...

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International Conference on algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI)

作者： Liu, Aiguo Miao, Guangyao Liu, Hui Li, Yefei Han, Yingzhu Liu, Dunnan Liu, Mingguang State Grid Ningxia Mkt Serv Ctr Metrol Ctr Yinchuan 750002 Ningxia Peoples R China North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

ISBN: (数字)9781510651890

ISBN: (纸本)9781510651890;9781510651883

This paper discusses the multi-objective optimization problem of how demand side resources respond flexibly to power grid dispatching based on market environment. Demand side resources dominated by large industrial users have great scheduling potential and response enthusiasm, especially flexible loads dominated by energy storage, adjustable loads and interruptible loads. Therefore, based on the analysis of the characteristics of flexible load participating in market aggregation, aiming at the minimum power abandonment of new energy and the lowest power consumption cost of users, comprehensively considering the power constraints of unit output and electrical equipment, the particle swarm optimization algorithm is used to verify and solve the example. Finally, the optimization goal of peak shaving and valley filling is realized to a certain extent, but there is still room for improvement.

关键词： demand side resource flexible load multi objective optimization particle swarm optimization algorithm

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Optimized Control Strategy for Photovoltaic Hydrogen Generation System with particle swarm algorithm

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ENERGIES 2022年第4期15卷 1472-1472页

作者： He, Hongyang Lu, Zhigang Guo, Xiaoqiang Shi, Changli Jia, Dongqiang Chen, Chao Guerrero, Josep M. Yanshan Univ Elect Engn Dept Qinhuangdao 066000 Hebei Peoples R China Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China State Grid Beijing Elect Power Co Beijing 100051 Peoples R China State Grid Jiaxing Elect Power Supply Co Jiaxing 314033 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

关键词： particle swarm optimization algorithm hybrid energy storage hydrogen generation system optimized energy storage capacity configuration

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Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets

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JOURNAL OF HYDROLOGY 2022年第PartA期612卷

作者： Xu, Hanbing Song, Songbai Guo, Tianli Wang, Huimin Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling 712100 Peoples R China

Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEELDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction resu

关键词： Two -stage hybrid model Langevin dynamics Bayesian neural networks Data portioning particle swarm optimization algorithm Yellow River basin

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A Systematic approach to improve Support Vector Machine applied to ultrasonic guided wave spectrum image classification

A Systematic approach to improve Support Vector Machine appl...

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IEEE International Ultrasonics Symposium (IEEE IUS)

作者： Miranda, Diego Olivares, Rodrigo Munoz, Roberto Minonzio, Jean-Gabriel Univ Valparaiso Escuela Ingn Informat Valparaiso Chile Univ Valparaiso Ctr Invest & Desarrollo Ingn Salud Valparaiso Chile

ISBN: (纸本)9780738112091

Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. optimization was done using the particle swarm optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.

关键词： Ultrasonic guided waves cortical bone fracture classification support vector machine particle swarm optimization algorithm

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Geothermal potential of a petroleum mature field using machine learning methods: Ecuadorian Amazon region

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Heliyon 2025年第3期11卷 e42216页

作者： Gómez, Franklin Vadászi, Marianna Faculty of Earth Science and Engineering University of Miskolc Egyetemváros Miskolc 3515 Hungary Petroleum Department Earth Science Faculty Escuela Politécnica Nacional Ladrón de Guevara E11⋅253 Quito 170517 Ecuador

Producing oil and extracting geothermal energy from the same reservoir is one type of the multigeneration systems that are garnering global attention. The aim of this research is to use machine learning methodologies to quantify the potential geothermal energy yield within a mature petroleum reservoir located in the Amazon region of Ecuador. The paper covers the complete geothermal simulation process of an area in the central-east region of Sacha Field. The study compares the performance of permeability calculation using the Kozeny and Carman algorithm in conjunction with Principal Component Analysis against permeability is calculated using the K-Nearest Neighbor density estimation algorithm. Historical matching is achieved using the particle swarm optimization algorithm, emphasizing a water production objective function. Additionally, the potential quantification compares the Volumetric method with Monte Carlo for the simulation of three scenarios until the year 2040. The results demonstrate excellent history matching using the Kozeny–Carman equation with Principal Component Analysis methodology. The historical production of water until the year 2009 was 2.625 million [std m3]¹ and the simulation result was 2.757 million [std m3]. The percentage of error in the volume is 5.02% with the historical production approach. A small area in the central east region of the reservoir has the potential to generate a maximum power of 6.5 MWt² based on the Volumetric methodology, which aligns with the average produced power of 0.66 MWt, depending on which of the three future scenarios up to 2040 is applied. © 2025 The Authors

关键词： Geothermal simulation K-nearest neighbor Monte Carlo technique particle swarm optimization algorithm Principal component analysis Sacha field Volumetric method

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Fault Diagnosis and Reliable Configuration of Uncertain Strip Area Based on MPSO-SVM 40

Fault Diagnosis and Reliable Configuration of Uncertain Stri...

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40th Chinese Control Conference (CCC)

作者： Li, Mochen Yao, Bo Wang, Fuzhong Shenyang Normal Univ Coll Math & Syst Sci Shenyang 110034 Liaoning Peoples R China Shenyang Inst Engn Dept Basic Educ Shenyang 110136 Liaoning Peoples R China

ISBN: (纸本)9789881563804

For a class of linear uncertain flight control systems, in terms of the strip region, the problem of actuator continuous gain fault and reliable control is studied. At the same time, in order to solve the difficulty of selecting parameters of support vector machine, a parameter optimization algorithm based on improved particle swarm optimization (MPSO) is proposed. Compared with grid search method, particle swarm optimization and genetic algorithm, this method has the advantages of fast convergence, short time consuming and high accuracy. For the problem that the pole information is difficult to obtain, the algorithm theory of pole observer is given to realize the real-time observation of unknown system. Finally, the effectiveness of reliable controller design is further proved by numerical simulation.

关键词： reliable control support vector machine pole observer particle swarm optimization algorithm grid search method genetic algorithm

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optimization of inbound vehicle routes in the collection of bio-medical wastes

Optimization of inbound vehicle routes in the collection of ...

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International Conference on Advances in Materials Research (ICAMR)

作者： Faizal, U. Mohamed Jayachitra, R. Vijayakumar, P. Rajasekar, M. Sri Shakthi Inst Engn & Technol Dept Mech Engn Coimbatore 641062 Tamil Nadu India PSG Coll Technol Dept Mech Engn Coimbatore 641062 Tamil Nadu India

In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and environment. In this article, the inbound logistics involved in the collection of Bio-Medical Waste at a Private Multi-Specialty Hospital located in Coimbatore which contains 59 wards has been improved to avoid time delay. An optimized vehicle routing model has been framed for a set of 6 dedicated vehicles with the objective to minimize the time taken during the collection of BMW. For this purpose a mathematical model is generated and solved using particle swarm optimization algorithm (PSO). The results infer that, by following the optimized vehicle routes, the time delay is totally eliminated and in addition the time taken for collecting the BMW is reduced by 42%, i.e. from 6 h to 3 h 46 min. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.

关键词： Vehicle routing Bio-medical waste Time delay Inbound logistics particle swarm optimization algorithm

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Research on RFID Virtual Tag Location algorithm Based on Monte Carlo 13

Research on RFID Virtual Tag Location Algorithm Based on Mon...

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IEEE 13th International Conference on Computer Research and Development (ICCRD)

作者： Guan, Tingliang Wang, Ding Su, Yixin CNNP Nucl Power Operat Management Co Ltd Dept Purchasing Management Jaxing Peoples R China Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665422604

Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation methods of virtual reference tag signal strength on positioning accuracy, and proposes a RFID virtual tag positioning algorithm based on Monte Carlo. The algorithm uses dynamic particles to replace the traditional static reference tag;introduces particle swarm optimization algorithm to update the Monte Carlo sample particle swarm, and gives different weights to the sampling particles based on the signal strength difference between the sampling particles and the undetermined tags, and finally completes the localization of the unknown tags through Monte Carlo resampling. The simulation results show that the algorithm can effectively improve the accuracy and stability of RFID positioning system compared with the traditional virtual tag positioning algorithm.

关键词： RFID VIRE algorithm Monte Carlo particle swarm optimization algorithm

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