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optimization of Model Predictive Control for Autonomous Vehicles Through Learning-Based Weight Adjustment

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Li, Haoran Lu, Yunpeng Li, Yaqiu Zheng, Sifa Sun, Chuan Zhang, Junyi Liu, Liqun Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan 430065 Peoples R China Tsinghua Univ Suzhou Automot Res Inst Suzhou 215134 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima 7390046 Japan Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Leeds Sch Comp Leeds LS2 9BW England

Model Predictive Control (MPC) method is widely used in autonomous vehicle control technology. The adjustment of MPC weights is crucial for optimizing its control performance, ensuring precise and reliable operation. Traditionally, these weights are adjusted manually, which is inefficient. This study introduces a novel butterfly optimization algorithm (BOA) learning-based method to determine the optimal MPC weights in an efficient way. By adopting the data-driven idea in machine learning, the trajectory data of field experiment human drivers is used to train the controller weights. A simulation-based training platform that enables the automatic training of the MPC controller with varying weights is also developed. Simulation results demonstrate the superior control accuracy and stability performance of BOA learning-based method compared to Linear Quadratic Regulator (LQR) and pure pursuit strategies. The findings suggest that the control method proposed in this research can significantly improve autonomous vehicle control performance and their reliability, thereby contributing to the advancement of autonomous driving technology.

关键词： optimization Autonomous vehicles Mathematical models Vehicle dynamics Accuracy Predictive models Machine learning algorithms Model predictive control autonomous vehicle butterfly optimization algorithm machine learning control theory

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Corporate bond coupon prediction based on deep learning

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2024年

作者： Liu, Tongyi Jia, Lifen Chen, Wei Capital Univ Econ & Business Sch Management & Engn Beijing Peoples R China

As the second largest bond market in the world, China's bond market has attracted extensive attention in recent years. Given its importance in facilitating financing arrangements and informing investment decisions, accurate bond coupon prediction is valuable. This paper proposes an ensemble model combining TabNet, DeepFM, and XGBoost for predicting the coupons of investment-grade corporate bonds. Specifically, to optimize the hyperparameters of the proposed model, an improved butterfly optimization algorithm incorporating the concepts of good point sets, refraction opposition-based learning, switching probability adjustment, and Solis & Wets search strategies is developed. Extensive experiments using data on China's investment-grade corporate bonds demonstrate the superior performance of the proposed model in the accuracy of bond coupon predictions. Additionally, the importance of various features has been discussed. The results show that the base interest rate for valuation and term to maturity are important to bond coupon predictions obtained by the proposed model.

关键词： Corporate bond coupon prediction butterfly optimization algorithm Solis & Wets method Tabnet DeepFM XGBoost

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A highly powerful calibration method for robotic smoothing system calibration via using adaptive residual extended Kalman filter

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2024年 86卷

作者： Deng, Yonghong Hou, Xi Li, Bincheng Wang, Jia Zhang, Yun Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn Chengdu 610054 Peoples R China Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Achieving high absolute positioning accuracy is crucial for obtaining aspheric optical components with remarkable surface quality using a robotic smoothing system. Robot kinematic calibration is an effective means of improving absolute positioning accuracy. The calibration algorithms that use gradient direction have been shown to significantly improve computational efficiency compared to other calibration methods. However, these algorithms usually suffer from gradient degradation or vanishing after several iterations. In particular, the extended Kalman filter depends on the initial covariance matrix, which must be continually adjusted to reasonable values using artificial means. To address this challenge, an adaptive residual extended Kalman filter is proposed for robot kinematic calibration. This method involves using the residual generated from the current iteration to avoid gradient degradation or vanishing in the next iteration. An improved butterfly optimization algorithm is also used to adapt the system covariance matrix, the covariance matrix of system noises, and the covariance matrix of measurement noises of the extended Kalman filter to improve the identification accuracy. Finally, the proposed method's feasibility is demonstrated through sufficient calibration experiments. The method improved the RMSE positioning accuracy from 0.9328 to 0.4786 mm, a 48.69 % increase from before calibration. The smoothing compensation experimental results show that the proposed method achieves optical components with excellent surface quality. The PV and RMS are, respectively 10.46 % and 20.96 % lower than before compensation, and the PSD curve is superior to before compensation.

关键词： Robot kinematic calibration Positioning accuracy Extended Kalman filter Residual butterfly optimization algorithm Ultra-precision smoothing

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Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115286-115286页

作者： Sowjanya, Kotte Injeti, Satish Kumar Kakatiya Inst Technol & Sci A Dept Elect & Commun Engn Warangal 506015 Telangana India Natl Inst Technol Warangal Dept Elect Engn Warangal 506004 Telangana India

Image processing is one of the major research regions in the most recent decades, in which, image segmentation has gained more popularity due to its enormous applications. Before segmenting an image, it has to be preprocessed using thresholding techniques. A properly thresholded image would give better segmentation results. Many researchers have contributed significant algorithms based on Otsu and Kapur based methods and reported outstanding results. These methods are suitable for bi-level as well as multilevel thresholding cases, however, the process of selecting the optimal thresholds in the case of multilevel thresholding is difficult and time consuming. To avoid this problem, this paper investigates the ability of two different algorithms namely: BOA (butterfly optimization algorithm) and GBMO (Gases Brownian Motion optimization) to determine the optimal threshold levels for image segmentation. The BOA is inspired from natural behaviour of butterflies which have special style of information propagation based on intensity of fragrance;whereas, GBMO is based on law of motion of gases which have greater ability to trade-off between exploration and exploitation. The candidate solutions were created using image histogram based on selected algorithms, and then they were updated according to the characteristics of each algorithm. The solutions are estimated using Otsu's between class variance as fitness function during the process of optimization. The performance of the proposed algorithms has been evaluated using various benchmark images and has been compared with seven different algorithms such as MFO (Moth Flame optimization), WOA (Whale optimization algorithm), SSO (Social Spider optimization), FASSO (Firefly Assisted Social Spider optimization), FA (Firefly algorithm), SCA (Sine Cosine algorithm), and HS (Harmony Search). The outcomes have been analysed based on various metrics like fitness value, PSNR and SSIM, as well as time complexity and the Wilcoxon statisti

关键词： Multilevel thresholding Grayscale images butterfly optimization algorithm Gases Brownian Motion optimization Qualitative and quantitative analysis

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Optimal design of RBFNN equalizer based on modified forms of BOA

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International Journal of Hybrid Intelligent Systems 2024年第Pre-press期Pre-press卷 1-14页

作者： Acharya, Badal Parida, Priyadarsan Panda, Ravi Narayan Mohapatra, Pradumya Department of Electronics and Communication Engineering GIET University Gunupur India Department of Electronics and Communication Engineering GIFT Bhubaneswar India Department of Electronics and Communication Engineering Vedang Institute of Technology Bhubaneswar India

The equalization of digital channels is widely recognized as a nonlinear classification problem. In such scenarios, utilizing networks that approximate nonlinear mappings can be highly advantageous. There has also been extensive research on equalizers based on Radial Basis Function Neural Networks (RBFNNs). This study introduces a training methodology centred on the Improved butterfly optimization algorithm (IBOA) for channel equalization using RBFNN. This approach aims to optimize the performance of RBFNN equalizers by leveraging the IBOA algorithm for training. Previous literature primarily approached the equalization problem as an optimization challenge. In contrast, this study addresses it as a classification problem. This training approach exhibits substantial enhancements compared to conventional metaheuristic algorithms.

关键词： Channel equalization RBFNN butterfly optimization algorithm

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Fatigue life prediction of welded joints based on multi-strategy improved butterfly algorithm 25

Fatigue life prediction of welded joints based on multi-stra...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering

作者： Xinyu Cao School of Railway Intelligent Engineering Dalian Jiaotong University Dalian Liaoning China

ISBN: (纸本)9798400712647

Currently, machine learning has been developed as an effective method for fatigue life prediction, but the accuracy on fatigue life prediction of welded joints under complex loads is still low. To solve this problem, this work proposes a butterfly optimization algorithm (BOA) based on multiple strategy improvement combined with kernel extreme learning machine (KELM) model for predicting the residual fatigue life of welded joints under two-level loading. Firstly, Circle chaotic mapping is is applied to improve the diversity of the initial population. And the dynamic switch probability and adaptive weight factor are introduced to effectively balance the global and local searches to avoid falling into the local optimum. Then the parameters of its optimized KELM are used to build the fatigue life prediction model of the improved BOA optimized KELM and compare it with other algorithm optimization models. The experimental results show that the prediction effect of the suggested model is better than other models and the prediction accuracy is high.

关键词： butterfly optimization algorithm

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Research on Raw Material JIT Procurement for Prefabricated Components Based on LMSMBOA algorithm 3

Research on Raw Material JIT Procurement for Prefabricated C...

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3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Zhao, Dengyu Wang, Heping Chen, Mengkai Anhui Univ Technol Sch Management Sci & Engn Maanshan Anhui Peoples R China

ISBN: (纸本)9781665417907

With a view to the procurement relationship between prefabricated component (PC) production enterprise and its upstream raw material supplier, an MRP-guided collaborative optimization model for order quantity in supply chain was constructed based on the thought of just-in-time (JIT) procurement by following the BOM constraint and MRP principle of PC production. The traditional butterfly optimization algorithm (BOA) was improved by combining Levy flight strategy, mixed mutation operation and simplex method (SM), thus proposing the LMSMBOA algorithm. In a practical case, the model was solved using the improved algorithm, which was then compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm and unimproved BOA. The results reflect that the LMSMBOA algorithm can obtain more outstanding raw material procurement schemes by solving the collaborative material procurement model in supply chain between PC production enterprises and upstream raw material suppliers.

关键词： prefabricated component JIT procurement collaborative optimization butterfly optimization algorithm

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Binary butterfly optimization approaches for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 116卷 147-160页

作者： Arora, Sankalap Anand, Priyanka DAV Univ Jalandhar Punjab India Lovely Profess Univ Jalandhar Punjab India

In this paper, binary variants of the butterfly optimization algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Binary butterfly optimization algorithm butterfly optimization algorithm Feature selection Bio-inspired optimization

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Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF

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SENSORS 2022年第18期22卷 6826-6826页

作者： Tang, Mingzhu Cao, Chenhuan Wu, Huawei Zhu, Hongqiu Tang, Jun Peng, Zhonghui Wang, Yifan Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.

关键词： fault detection butterfly optimization algorithm extreme random forest wind turbine gearbox

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Near-Ground Delivery Drones Path Planning Design Based on BOA-TSAR algorithm

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DRONES 2022年第12期6卷 393-393页

作者： Luo, Yuan Lu, Jiakai Zhang, Yi Zheng, Kai Qin, Qiong He, Lin Liu, Yanyu Chongqing Univ Posts & Telecommun Key Lab Optoelect Informat Sensing & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

With the advancement of technology and the rise of the unmanned aerial vehicle industry, the use of drones has grown tremendously. For drones performing near-ground delivery missions, the problem of 3D space-based path planning is particularly important in the autonomous navigation of drones in complex spaces. Therefore, an improved butterfly optimization (BOA-TSAR) algorithm is proposed in this paper to achieve the autonomous pathfinding of drones in 3D space. First, this paper improves the randomness strategy of the initial population generation in the butterfly optimization algorithm (BOA) via the Tent chaotic mapping method, by means of the removal of the short-period property, which balances the equilibrium of the initial solutions generated by the BOA algorithm in the solution space. Secondly, this paper improves the shortcomings of the BOA algorithm in terms of slower convergence, lower accuracy, and the existence of local optimal stagnation when dealing with high-dimensional complex functions via adaptive nonlinear inertia weights, a simulated annealing strategy, and stochasticity mutation with global adaptive features. Finally, this paper proposes an initial population generation strategy, based on the 3D line of sight (LOS) detection method, to further reduce the generation of path interruption points while ensuring the diversity of feasible solutions generated by the BOA algorithm for paths. In this paper, we verify the superior performance of BOA-TSAR by means of simulation experiments. The simulation results show that BOA-TSAR is very competitive among swarm intelligence (SI) algorithms of the same type. At the same time, the BOA-TSAR algorithm achieves the optimal path length measure and smoothness measure in the path-planning experiment.

关键词： drones butterfly optimization algorithm path planning tent chaotic mapping adaptive nonlinear inertia weights simulated annealing adaptive random mutation

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