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Prediction and design of high hardness high entropy alloy through machine learning

MATERIALS & DESIGN 2023年 235卷

作者： Ren, Wei Zhang, Yi-Fan Wang, Wei-Li Ding, Shu-Jian Li, Nan Northwestern Polytech Univ Xian 710072 Peoples R China Xian Univ Posts & Telecommun Sch Sci Xian 710121 Peoples R China

Two data-driven machine learning (ML) models were proposed for the hardness prediction of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs, respectively. The hardness prediction model combined interpretable ML methods with solid solution strengthening theory, and the R2 and RMSE values of 0.9716 and 39.2525 were respectively achieved under the leave-one-out validation method. The optimization model adopted an intelligent optimization algorithm to design the optimized elemental molar ratios of high hardness HEAs and was experimentally verified. A general design framework was summarized for prediction and composition optimization of various HEA performances.

关键词： High-entropy alloy Machine learning intelligent optimization algorithm Interpretable machine learning Solid solution strengthening

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Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA

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SENSORS 2023年第15期23卷 6741-6741页

作者： Kong, Lingchao Liang, Hongtao Liu, Guozhu Liu, Shuo Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

关键词： wind turbine CatBoost algorithm fault detection category imbalance intelligent optimization algorithm

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A novel mutual aid Salp Swarm algorithm for global optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第17期35卷

作者： Zhang, Huanlong Feng, Yuxing Huang, Wanwei Zhang, Jie Zhang, Jianwei Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Coll Software Zhengzhou Peoples R China

Salp Swarm algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm algorithm, we proposed a Salp Swarm algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame optimization, Grasshopper optimization, and Ant Lion optimization.

关键词： intelligent optimization algorithm mutual learning mechanism Salp Swarm algorithm tangent function

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Design and optimization of Solar Photovoltaic Brackets in Mountainous Areas Adapted to Complex Wind Environments

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Asian Agricultural Research 2024年第12期16卷 22-25页

作者： Aijun LIN Junwen TANG Zhejiang Huadong Engineering Consulting Co. Ltd.Hangzhou 310030China

With the increasing global demand for renewable energy,the application of photovoltaic power generation in mountainous areas is gradually ***,the complex wind environment in mountainous areas poses severe challenges to the design and optimization of solar photovoltaic *** design methods are difficult to cope with the changeable wind speed and direction in mountainous areas,resulting in structural instability or material *** have identified the key factors affecting wind response through parametric research and dynamic wind response analysis,so as to optimize the brackets design and improve its adaptability and stability in complex wind *** this paper,the complexity of wind speed,wind direction and turbulence characteristics in mountainous areas and their influence on brackets design are *** static and dynamic wind load analysis,the geometrical shape and material selection of the bracket are optimized to enhance its wind *** application of multi-objective optimization model and intelligent optimization algorithm provides an effective solution for the design of solar photovoltaic brackets,ensuring their safety and reliability in complex wind environments.

关键词： Solar photovoltaic brackets Complex wind environment optimization design intelligent optimization algorithm

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Population-based discrete state transition algorithm with decomposition and knowledge guidance applied to electrolytic cell maintenance decision

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APPLIED SOFT COMPUTING 2023年 135卷

作者： Shi, Jue Chen, Xiaofang Xie, Yongfang Zhang, Hongliang Sun, Yubo Cent South Univ Sch Automation Changsha Peoples R China Cent South Univ Sch Met & Environm Changsha Peoples R China

With continuous scale expansion and higher safety requirements in modern aluminum electrolysis, it is more and more necessary to realize an intelligent decision-making for shutting-down and running of aluminum reduction cells (SRARC). However, this realization needs to solve a special single-objective constrained integer optimization problem (SCIOP), where the key challenge is a high requirement for the constraint-handling ability. In this paper, based on a high-scalability intelligent optimization algorithm called discrete state transition algorithm (DSTA), a population-based DSTA with decomposition and knowledge guidance (PDSTA/D-S) is proposed. This PDSTA/D-S improves the constraint-handling ability of DSTA from three aspects. Firstly, a hybrid framework combining DSTA with genetic algorithm is proposed. Secondly, a decomposition-based multi-objective optimization for constrained problems with uniformly-angled weighted sum vector is proposed. Thirdly, the manual decision-making is transferred to a knowledge-based transformation operator of DSTA. Therefore, a high-level performance of decision-making for SRARC can be obtained. The related experiments on a SRARC which is built from the practical production have demonstrated that the proposed PDSTA/D-S not only makes an effective improvement of DSTA from three aspects, but also has a more advanced performance compared with other existing high-performance intelligent optimization algorithms.(c) 2023 Published by Elsevier B.V.

关键词： Aluminum electrolysis Decision-making of shutting-down and running intelligent optimization algorithm Discrete state transition algorithm Genetic algorithm

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Application Deep Extreme Learning Machine in Multi-dimensional Smart Teaching Quality Evaluation System

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第2期11卷 1-10页

作者： Li, Yanan Nan, Fang Zhang, Hao North China Univ Sci & Technol Qinggong Coll Tangshan 063000 Hebei Peoples R China

INTRODUCTION: The construction of the wisdom teaching evaluation system, as the essential part of the institution's teaching reform, is conducive to developing the institution's disciplines, making the existing teaching more standardized, and making the means of teaching diversified, intelligent, and ***: Aiming at the current intelligent teaching evaluation design method, there are evaluation indexes that need to be more comprehensive, a single method, and system standard ***: Proposes an intelligent optimization algorithm for a multi-dimensional innovative teaching quality evaluation method. First of all, the multi-dimensional wisdom teaching evaluation system is constructed by analyzing the influencing factors of teaching quality evaluation;then, the parameters of the depth limit learning machine are optimized by the bird foraging search algorithm, and the multi-dimensional wisdom teaching evaluation model is constructed;finally, the validity and stability of the proposed method are verified by the analysis of simulation ***: The results show that the proposed method improves the accuracy of the evaluation ***: Solves the problem of low evaluation accuracy and incomplete system of teaching quality evaluation methods.

关键词： competent teaching quality evaluation multi-dimensional deep limit learning machine intelligent optimization algorithm

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Migration-based algorithm library enrichment for constrained multi-objective optimization and applications in algorithm selection

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INFORMATION SCIENCES 2023年第1期649卷

作者： Wang, Yan Zuo, Mingcheng Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

It is of necessity to select appropriate optimization algorithms from an algorithm library due to the universality of constrained multi-objective optimization problems and the suitability of intelligent optimization algorithms, which requires a rich optimization algorithm library. This paper proposes a migration-based method of enriching the algorithm library for constrained multi-objective optimization problems. After calculating the similarity between problems based on their landscape features, the proposed method calculates the migration probabilities of intelligent optimization algorithms solving similar problems based on the performance of each algorithm and the similarity between problems. According to the redundancy and compatibility of components, the algorithms with large migration probabilities enrich the algorithm library for solving the current problem. Based on the enhanced algorithm library, a Softmax regression model is trained to generate an optimal intelligent algorithm to solve the current problem. The proposed method is applied to solve a series of constrained multi-objective optimization benchmark problems and the operation optimization problems of an integrated coal mine energy system, and the experimental results verify its effectiveness and feasibility.

关键词： Constrained multi-objective optimization intelligent optimization algorithm algorithm library enrichment Migration Integrated coal mine energy system

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A Compensation Method for Random Error of Gyroscopes Based on Support Vector Machine and Beetle Antennae Search algorithm

A Compensation Method for Random Error of Gyroscopes Based o...

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International Conference on Computer, Control and Robotics (ICCCR)

作者： Wang, Pengfei Li, Guangchun Gao, Yanbin Harbin Engn Univ Dept Intelligent Sci Syst & Engn Harbin Peoples R China

ISBN: (纸本)9781728190358

Gyroscope is a common inertial sensor for inertial navigation system(INS). Its accuracy is affected by various error sources and noises. Among them, the deterministic error can be eliminated by calibration. However, random error is difficult to be evaluated due to its uncertainty, so random error has a great negative impact on the accuracy of gyroscope. Therefore, in order to improve the precision of gyroscope, it is necessary to compensate the random error. This paper proposes a compensation method for random error of gyroscopes based on support vector machine (SVM) and beetle antennae search (BAS) algorithm. Firstly, the original time series data of random error is processed in groups by a slide window, and then the SVM model is trained by using the original data of random error. The two key parameters in SVM are optimized by BAS algorithm. Finally, The trained SVM model is used to predict the gyroscopes random error data, and the prediction results are used to complete the compensation of gyroscopes data. Experimental results show the effectiveness of the proposed scheme, and the compensation accuracy is better than the traditional autoregressive integrated moving average (ARIMA) method.

关键词： gyroscope random error compensation intelligent optimization algorithm beetle antennae search support vector machine

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A Novel Feature Selection Method Based on Salp Swarm algorithm

A Novel Feature Selection Method Based on Salp Swarm Algorit...

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IEEE International Conference on Information Communication and Software Engineering (ICICSE)

作者： Yan, Chaokun Suo, Zhihao Guan, Xinyu Luo, Huimin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China

ISBN: (纸本)9781665419192

Biomedical and clinical data usually contain some redundant and irrelevant features, which may lead to misleading and over-fitting problems in the process of modeling algorithms. In order to effectively remove irrelevant or redundant features, the use of feature selection methods can reduce the number of features, improve the accuracy of the model, and reduce the running time. In recent years, Wrapper-based feature selection algorithms have received widespread attention because they can obtain better accuracy. This paper uses a wrapper feature selection algorithm FS_SSA based on Salp swarm. In the FS_SSA algorithm, the position of the follower salps is updated by the relative position of the Salp. The followers gradually moves to the leading Salp. The gradual movement of the follower salps can make the Salp Swarm algorithm not easy to fall into a local optimal state. The two behaviors of exploration and development in subset searches are balanced, and the search process of feature subsets is prevented from falling into the local optimum. Experimental results based on public medical data sets show that the FS_SSA has better classification performance than other methods.

关键词： feature selection intelligent optimization algorithm salp swarm algorithm

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An analysis of time commitment for college students to online English writing and peer assessment learning

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Wu, Meng Beijing Technol & Business Univ Sch Foreign Languages Beijing 100048 Peoples R China

Online English writing and peer assessment have always occupied an important place in the assessment of composition at the basic education level as an important writing style and assessment tool. Therefore, this paper constructs the IOA-English analysis model based on the intelligent optimization algorithm as the theoretical basis. An in-depth study of online English writing patterns and the time devoted to them, and the intensity and effectiveness of peer assessment among college students. The results of the study are as follows: the computational analysis shows that the time required for the pre-writing preparation stage is 5-10 minutes, with a difficulty factor of 0.3. The final draft submission phase required the least amount of time, 3-7 minutes, with a difficulty factor of only 0.1. The writing revision stage, the most important stage, takes 10-15 minutes and has a difficulty factor of 0.6. The time for the writing revision phase can be divided into two parts: the first part is the time for tasting the model essay, which is about 3-8 minutes. The second part is the writing and revision time, which is about 12-17 minutes. More than 91% of the students were aware of the importance of peer assessment and were brave enough to express their opinions in cooperative learning and to take care of each other's learning needs in peer assessment.

关键词： English writing Peer-to-peer assessment Pattern analysis intelligent optimization algorithm Online writing

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