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Development of Fixture Layout optimization for Thin-Walled Parts:A Review

Chinese Journal of Mechanical Engineering 2024年第1期37卷 15-39页

作者： Changhui Liu Jing Wang Binghai Zhou Jianbo Yu Ying Zheng Jianfeng Liu School of Mechanical Engineering Tongji UniversityShanghai 201804China Shanghai Jiaotong University Shanghai 200240China Shanghai Waigaoqiao Shipbuilding Co. Ltd.Shanghai 200137China

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.

关键词： Thin-walled parts Assembly quality Fixture layout optimization Modeling methods 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 understand.The 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 factors.Gradient 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 variables.Shap values and two-dimensional PDP analysis were then carried out.Engineers 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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Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1595-1617页

作者： Ji Zhou Yijun Lu Qiong Tian Haichuan Liu Mahdi Hasanipanah Jiandong Huang College of Civil and Environmental Engineering Hunan University of Science and EngineeringYongzhou425006China Hunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts Hunan University of Science and EngineeringYongzhou425006China School of Civil Engineering Guangzhou UniversityGuangzhou510006China Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam School of Engineering&Technology Duy Tan UniversityDa Nang550000Vietnam

Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

关键词： Flyrock induced by blasting optimization algorithms SVR GRNN

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optimization algorithms for Flexible Protein-Protein Docking

Optimization Algorithms for Flexible Protein-Protein Docking

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第三届数字制造与自动化国际会议

作者： Zhao Li Zhang Tianchi Zhang Jing College of Computer Science and Technology Harbin Engineering University Department of computer science and Information Engineering Harbin Normal University

The flexibility in protein docking is a major challenge in bioinformatic research.. Protein docking problem is essentially an optimization problem. In this review we describe the methods of existing flexible protein-protein docking ,focusing on the optimization algorithms . We divide the different methods into categories for presenting clearly.

关键词： flexible docking optimization algorithms protein-protein docking

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optimization-inspired manual architecture design and neural architecture search

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Science China(Information Sciences) 2023年第11期66卷 100-112页

作者： Yibo YANG Zhengyang SHEN Huan LI Zhouchen LIN JD Explore Academy School of Mathematical Sciences Peking University Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University Institute for Artificial Intelligence Peking University Pazhou Lab

Neural architecture has been a research focus in recent years due to its importance in deciding the performance of deep networks. Representative ones include a residual network(Res Net) with skip connections and a dense network(Dense Net) with dense connections. However, a theoretical guidance for manual architecture design and neural architecture search(NAS) is still lacking. In this paper, we propose a manual architecture design framework, which is inspired by optimization algorithms. It is based on the conjecture that an optimization algorithm with a good convergence rate may imply a neural architecture with good performance. Concretely, we prove under certain conditions that forward propagation in a deep neural network is equivalent to the iterative optimization procedure of the gradient descent algorithm minimizing a cost function. Inspired by this correspondence, we derive neural architectures from fast optimization algorithms,including the heavy ball algorithm and Nesterov’s accelerated gradient descent algorithm. Surprisingly, we find that we can deem the Res Net and Dense Net as special cases of the optimization-inspired architectures.These architectures offer not only theoretical guidance, but also good performances in image recognition on multiple datasets, including CIFAR-10, CIFAR-100, and Image Net. Moreover, we show that our method is also useful for NAS by offering a good initial search point or guiding the search space.

关键词： deep neural network manual architecture design neural architecture search image recognition optimization algorithms learning-based optimization

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Achieving geometric convergence for distributed optimization with Barzilai-Borwein step sizes

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Science China(Information Sciences) 2022年第4期65卷 280-281页

作者： Juan GAO Xin-Wei LIU Yu-Hong DAI Yakui HUANG Peng YANG School of Artificial Intelligence Hebei University of Technology Institute of Mathematics Hebei University of Technology LSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

Dear editor,We consider distributed optimization, which can be formulated to minimize the average of all local objective functions:■where each local function f;:■ is L;-smooth and μ;-strongly convex and known only ... 详细信息

关键词： distributed optimization multi-agent network Barzilai-Borwein step sizes optimization algorithms convergence rate

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Supercritical thermophysical properties prediction of multi-component hydrocarbon fuels based on artificial neural network models

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Science China(Technological Sciences) 2022年第4期65卷 903-919页

作者： MING ZhenYang LIU HaiFeng WANG QianLong YUE ZongYu CUI YanQing WEN MingSheng YAO MingFa State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

A good understanding of the thermophysical properties of hydrocarbon fuels at supercritical pressure is important to research on experiment and numerical simulation of fuel supercritical spray.Experimental measurements are difficult to conduct directly because of the extremely high pressure and high temperature.In this study,back propagation(BP)neural network,BP optimized by mind evolution algorithm(MEA-BP)and BP neural network optimized by genetic algorithm(GA-BP)are established to determine the nonlinear temperature-dependent thermophysical properties of density,viscosity,and isobaric specific heat(C_(2))of hydrocarbon fuels at supercritical pressure.Meanwhile,approximate formulas for these properties prediction are primarily proposed using polynomial fitting.In this paper,models that can predict three types of physical properties of three kinds of hydrocarbon fuels and their mixtures in a wide temperature range under supercritical pressure are established.In the prediction of density and C_(2),BP neural network has a good prediction effect.The results show that the MAPE is lower than 2%in the prediction of density and C_(2),but the MAPE of viscosity prediction is slightly higher than 5%using BP.Furthermore,MEA and GA are used to optimize the prediction of viscosity.The optimization effect and computation of the MEA is better than that of GA because MEA does not have the local optimization and prematurity problems.The present work offers an efficient tool to predict the thermophysical properties of hydrocarbon fuels over a wide range of temperatures under supercritical pressure which can be easily extended to other fuels of interest.It will be beneficial to the experiment and numerical simulation studies of supercritical sprays.

关键词： alternative fuel supercritical spray back propagation neural network optimization algorithms thermophysical properties

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A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

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Computers, Materials & Continua 2022年第3期70卷 6239-6255页

作者： Minakshi Kalra Vijay Kumar Manjit Kaur Sahar Ahmed Idris Saban Oturk Hammam Alshazly Computer Science Department Government College BahadurgarhBahadurgarh124507India CSE Department National Institute of Technology Hamirpur177005India School of Engineering and Applied Sciences Bennett UniversityGreater Noida201310India College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Department of Electrical and Electronics Engineering Amasya UniversityAmasyaTurkey Faculty of Computers and Information South Valley UniversityQena83523Egypt

Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors increases.optimizationmethods offermore practical solutions instead of exact solutions for the solution of this problem.The Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins.It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features.Although traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search space.Therefore,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature problems.BEPO algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search space.For this purpose,the sigmoidal functions are preferred in determining the emperor positions.In addition,the boundaries of the search space remain constant by choosing binary operators.BEPO’s performance is evaluated over twenty-nine benchmarking functions.Statistical evaluations are made to reveal the superiority of the BEPO algorithm.In addition,the performance of the BEPO algorithm was evaluated for the binary feature selection problem.The experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.

关键词： Metaheuristics optimization algorithms emperor penguin optimizer intensification diversification feature selection

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Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

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Computers, Materials & Continua 2022年第10期73卷 87-101页

作者： Bassam A.Y.Alqaralleh Fahad Aldhaban Anas Abukaraki Esam A.Al.Qaralleh MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal UniversityMa’an71111Jordan School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan

Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become essential.In this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)technique.The intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus images.In addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions.Moreover,the fusion of two DL models namely,CapsNet and MobileNet is used for feature extraction.Further,the hyperparameter tuning of these models is also performed via Coyote optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify DR.The experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.

关键词： optimization algorithms medical images diabetic retinopathy deep learning fusion model

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On Comparing Six optimization algorithms for Network-based Wind Speed Forecasting

On Comparing Six Optimization Algorithms for Network-based W...

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第37届中国控制会议

作者： Huanjie Tao Xiaobo Lu School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

Network-based wind speed forecasting has played an important role in the power system. The network parameters optimization is an important issue, and different optimization algorithms are believed to result in different forecasting accuracies. In this paper, six network parameters optimization algorithms, including Gradient descent, Momentum, Ada Grad, RMSprop, Adam, and Adadelta, are implemented and compared in the application of wind speed forecasting. As a case study, this paper uses a wind speed data obtained from Ningxia, China. The performance is evaluated by three metrics, namely, mean absolute error（MAE）, root mean square error（RMSE）, and mean absolute percentage error（MAPE）. The experiment results show that, Adam algorithm and RMSprop algorithm achieve better forecasting accuracy and less training time than the other optimization algorithms. This study can be a guide to the selection of optimization algorithms on wind speed forecasting problems for researchers.

关键词： Wind speed forecasting Back-propagation neural network Forecasting accuracy optimization algorithms

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