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optimization algorithms for composite beam as smart active control of structures using genetic algorithms

SMART STRUCTURES AND SYSTEMS 2021年第6期27卷 1041-1052页

作者： Cao, Yan Zandi, Yousef Gholizadeh, Morteza Fu, Leijie Du, Jiang Qian, Xueming Wang, Zhijie Roco-Videla, Angel Selmi, Abdellatif Issakhov, Alibek Xian Technol Univ Sch Mech Engn Xian 710021 Peoples R China Islamic Azad Univ Tabriz Branch Dept Civil Engn Tabriz Iran Univ Bernardo OHiggins Fac Ciencias Salud Programa Magister Ciencias Quim Biol Santiago Chile Univ Catolica Santisima Concepcion Fac Ingn Dept Ingn Civil Concepcion Chile Prince Sattam Bin Abdulaziz Univ Dept Civil Engn Coll Engn Al Kharj 11942 Saudi Arabia Ecole Natl DIngenieurs Tunis ENIT Civil Engn Lab BP 37 Tunis 1002 Tunisia Al Farabi Kazakh Natl Univ Alma Ata Kazakhstan Kazakh British Tech Univ Alma Ata Kazakhstan

The principles of productive active and semi-active civil and infrastructure engineering structural control date back 40 years and significant progress has been recorded in those four decades. Smart structures typically have some control systems that enable them to deal with perturbations. The active vibration management techniques have been applied numerically and experimentally in order to reduce the vibrational levels of lightweight economic composite structures. Smart composite beams and plates have been produced and tested with surface-based piezoelectric sensors and actuators. It has been found that an effective model of smart composite plates can predict the dynamic characteristics. Utilizing Genetic Algorithm (GA) was designed and implemented. Two regression model as root mean square (RMSE) and determination coefficient (R2) were used. The first and second bending modes are operated effectively by a beam, and simultaneous vibration levels are significantly reduced for the conductive plates by the simultaneous operation of the bending and twisting modes. Vibration management is realized by using efficient control. GA could show better performance for managing linear feedback laws under given assumptions.

关键词： active control optimization algorithms composite beam genetic algorithms

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optimization algorithms for Wireless Sensor Networks Node Localization: An Overview

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IEEE ACCESS 2024年 12卷 50459-50488页

作者： Ahmad, Rami Alhasan, Waseem Wazirali, Raniyah Aleisa, Noura Amer Univ Emirates Coll Comp Informat Technol Dubai U Arab Emirates Berlin Sch Business & Innovation GmbH D-12043 Berlin Germany Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means of enhancing the WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes, and offers a comprehensive review of the optimization algorithms used for this purpose. This review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors such as localization accuracy, scalability, computational complexity, and robustness were systematically evaluated and compared across various optimization algorithms. Additionally, the study sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as valuable resources for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.

关键词： Wireless sensor networks Location awareness optimization Surveys Particle swarm optimization Protocols Network topology Three-dimensional displays WSNs-6LoWPAN localization optimization algorithms 2D 3D irregular surfaces

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optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第10期36卷 3011-3039页

作者： Ahmed, A. A. Masrur Ahmed, Mohammad Hafez Saha, Sanjoy Kanti Ahmed, Oli Sutradhar, Ambica Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia West Virginia Univ Dept Civil & Environm Engn POB 6103 Morgantown WV 26506 USA Norwegian Univ Sci & Technol Dept Civil & Environm Engn Trondheim Norway Leading Univ Sch Modern Sci Sylhet 3112 Bangladesh

The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

关键词： Deep learning Hybrid model Solar ultraviolet index optimization algorithms Public health

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optimization algorithms: Who own the Crown in Predicting Multi-Output Key Performance Index of LTE Handover

Optimization Algorithms: Who own the Crown in Predicting Mul...

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2022 International Conference on Automatic Control and Intelligent Systems

作者： Allias, Noormadinah Noor, Megat Norulazmi Megat Mohamed Ismail, Mohd Taha Ismail, Mohd Nazri Univ Kuala Lumpur Dept Comp Engn Technol Malaysian Inst Informat Technol 1016 Jalan Sultan Ismail Kuala Lumpur 50250 Malaysia Albukhary Int Univ Sch Comp & Informat Jalan Tun Razak Alor Setar 05200 Kedah Malaysia Natl Def Univ Malaysia Fac Def Sci & Technol Kuala Lumpur 57000 Malaysia

ISBN: (纸本)9781665495813

The Long-Term Evolution network (LTE) has been introduced to cater to the rich content applications of multimedia services. With its ability to support lower latency and higher Throughput, the LTE network can provide faster data download speeds. However, once the mobile user moves from one location to another, the performance tends to degrade. Thus, it required the handover from the serving base station to the target base station. Therefore, the telecommunication service providers must provide a further service enhancement to increase the network quality. As a result, the Key Performance Index (KPI) modeling and predictions can be utilized to achieve this objective. In this article, the Extreme Gradient Boosting regressor algorithm has been selected. However, the hyper-parameter associated with this algorithm needs to be optimized first to produce good prediction results. Three optimization algorithms have been chosen: the Annealing Search, Random Search, and the Tree Parzen Estimator. The experiment results show that the Extreme Gradient Boosting with Annealing Search outperformed the Random Search and the Tree Parzen Estimator by producing the lowest MAE and RMSE and higher R-2.

关键词： LTE Key Performance Index (KPI) Extreme Gradient Boosting optimization algorithms

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optimization algorithms for Integrated Processes in Industry 4.0

Optimization Algorithms for Integrated Processes in Industry...

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IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)

作者： Leite, Mario Romero, Fernando Alves, Claudio Pinto, Telmo Univ Minho Ctr ALGORITMI Dept Prod & Syst Braga Portugal Univ Minho Ctr ALGORITMI Dept Prod & Syst Guimaraes Portugal Univ Coimbra CEMMPRE FCTUC Coimbra Portugal

ISBN: (纸本)9781728159508

Evidence of an increasingly dynamic market forces companies to look for new ways to respond, given that traditional supply chain management systems are increasingly more vulnerable in their needs. In this sense, we have seen a paradigm shift at the industrial level with the emergence of new concepts from Industry 4.0 to improve productivity and process efficiency. However, their implementation in companies can be an expensive and time-consuming process, particularly for small and medium-sized enterprises. This work presents a perspective for optimization algorithms in the context of Industry 4.0. With new methods and models, simultaneously integrating traditional supply chain processes, it is possible to find good solutions (globally optimal), in real time and with an investment cost more proportional to the reality of each company. It may, therefore, be an alternative to mitigate the discrepancy between companies of quite different sizes.

关键词： Industry 4.0 Supply chain optimization algorithms optimization of integrated processes

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optimization algorithms in Reconstructions of Neuron Morphology: An Overview 12

Optimization Algorithms in Reconstructions of Neuron Morphol...

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IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green Computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of Things (iThings) / 5th IEEE Int Conf on Smart Data

作者： Liu, Chao Zheng, Nenggan Zhao, Ting Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China Zhejiang Univ Qiushi Acad Adv Studies Hangzhou Peoples R China Howard Hughes Med Inst Jenelia Res Campus Ashburn VA 20147 USA

ISBN: (纸本)9781728129808

Neuron morphological reconstruction is the momentousness in the study of brain connectome which can extract the types of neurons and the connection patterns on the micron scale. Due to the vast data volume of neurons, complex neuron structure and the reconstruction of neuron population, the reconstruction of neuron is challenging. This paper reviews the current research status of neuron reconstruction. The state-of-the-art optimization algorithms in neuron reconstruction methods are summarized. We divide the process of neuron reconstruction into three steps based on the function of the optimization algorithm, which is branch tracing, branch merging and branch adjustment. What's more, we propose a framework of the optimization algorithm and decompose the objective functions into internal energy terms and external energy terms.

关键词： Neuron morphology reconstruction optimization algorithms Objective function Internal energy terms External energy terms

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Leveraging Ant Colony and Particle Swarm optimization algorithms for Assessing the Transit-Oriented Development Potential: A Case Study of Dhaka, Bangladesh

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Journal of Advanced Transportation 2025年第1期2025卷

作者： Md. Anwar Uddin Sumit Roy Tahsin Tamanna Rubayet Arafin Rimon Military Institute of Science and Technology Dhaka 1216 Bangladesh mist.ac.bd Bangladesh University of Engineering and Technology Dhaka 1205 Bangladesh buet.ac.bd Rajshahi University of Engineering and Technology Rajshahi 6204 Bangladesh ruet.ac.bd

This study explores transit-oriented development (TOD) in Dhaka City using optimization algorithms to provide urban planning and policy-making insights. The analysis examined the distribution of the TOD index values across the city and identified areas with varying levels of TOD potential. Two optimization algorithms, ant colony optimization (ACO) and particle swarm optimization (PSO), were employed to assess and compare the TOD index values. The results highlight the significance of transit infrastructure in promoting sustainable urban development, particularly in proximity to existing mass rapid transit (MRT) lines. PSO is more suitable for this study among the optimization algorithms because it offers a more precise TOD potential assessment. The findings suggest prioritizing investments in transit infrastructure and implementing TOD-friendly policies to foster sustainable urban growth and improve residents’ quality of life. Future studies can benefit from optimizing the algorithm parameters and incorporating real-world data to improve the accuracy of the TOD assessments.

关键词： ant colony optimization optimization algorithms particle swarm optimization sustainable urban planning transit-oriented development

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optimization algorithms vs. random sampling of entry sources for a deliberate food contamination 34

Optimization algorithms vs. random sampling of entry sources...

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34th GIL Conference on Modern Information Technology in the Agriculture, Forestry and Food Sectors

作者： Pinior, Beate Selhorst, Thomas Institute for Veterinary Public Health University of Veterinary Medicine Vienna Veterinärplatz 1 Wien1210 Austria Friedrich-Loeffler-Institut Institute of Epidemiology Seestraße 55 Wusterhausen16868 Germany

ISBN: (纸本)9783885796206

We focus on a deliberate scenario, where milk producers are used as entry sources for a contamination and where milk consumers are the target of the attack. The aim of this study is to demonstrate how the size of damage differs dependent on the use of an optimization algorithm or a random selection of entry sources. The results indicate that with a random selection of entry sources the same results can be provided with respect to the number of consumers reached, as with the application of the greedy algorithm. However, it should be also noted that with random selection of entry sources there is also a possibility of selecting milk producers, which would not reach any consumer with the hypothetical contaminated milk. The résumé is that by using the greedy algorithm always the "best" suited milk producers will be selected for a maximum spread of contaminated milk in our model. Risk managers can use these results in order to select the sources of entry in a time-and resource efficient manner.

关键词： optimization algorithms

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Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

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Frontiers of Structural and Civil Engineering 2022年第10期16卷 1267-1286页

作者： Thuy-Anh NGUYEN Hai-Bang LY Van Quan TRAN Department of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately *** primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams(SB).A database encompassing 1118 experimental findings from the relevant literature was compiled,containing eight distinct *** Boosting(GB)technique was developed and evaluated in combination with three different optimization algorithms,namely Particle Swarm optimization(PSO),Random Annealing optimization(RA),and Simulated Annealing optimization(SA).The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output *** values and two-dimensional PDP analysis were then carried *** may use the findings in this work to define beam’s geometrical components and material used to achieve the desired shear strength of SB without reinforcement.

关键词： slender beam shear strength gradient boosting optimization algorithms

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Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms

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INFORMATION SCIENCES 2015年 299卷 337-378页

作者： Ardizzon, Guido Cavazzini, Giovanna Pavesi, Giorgio Univ Padua Dept Ind Engn I-35131 Padua Italy

The paper presents a novel paradigm of the original particle swarm concept, based on the idea of having two types of agents in the swarm;the "explorers" and the "settlers", that could dynamically exchange their role in the search process. The explorers' task is to continuously explore the search domain, while the settlers set out to refine the search in a promising region currently found by the swarm. To obtain this particle task differentiation, the numerical coefficients of the cognitive and social component of the stochastic acceleration as well as the inertia weight were related to the distance of each particle from the best position found so far by the swarm, each of them with a proper distribution over the swarm. This particle task differentiation enhances the local search ability of the particles closer to gbest and improves the exploration ability of the particles as the distance from gbest increases. The originality of this approach is based on the particle task differentiation and on the dynamical adjustment of the particle velocities at each time step on the basis of the current distance of each particle from the best position discovered so far by the swarm. To ascertain the effectiveness of the proposed variant of the PSO algorithm, several benchmark test functions, both unimodal and multi-modal, have been considered and, thanks to its task differentiation concept and adaptive behavior feature, the algorithm has demonstrated a surprising effectiveness and accuracy in identifying the optimal solution. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Particle swarm optimization Swarm intelligence optimization algorithms

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