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154th Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, TMS 2025

作者： Li, Cheng-Hong Zhu, Ming-Mei Zhang, Xian-Wu Yang, Zhen-Jiang Jiang, Kun-Chi Jiang College of Materials Science and Engineering Chongqing University Chongqing 400044 China

ISBN: (纸本)9783031807473

The cooling efficiency and quality of continuous casting slab are closely related to the volume of secondary cooling water, and the optimization of the secondary cooling water volume in the secondary cooling zone is the guarantee of slab quality. In this paper, minimizing the average temperature difference between the surface center temperature and the target temperature of each cooling zone is taken as the objective function, and the metallurgical criteria are taken as constraints. Differential evolution, particle swarm optimization and firefly algorithm are used to establish the optimal model of secondary cooling water volume. Those models are verified with the production data of a steel plant. The results show that the average temperaturedifferenceofPSOisthelowest,whichis3.9°*** parameters of PSO, the final average temperature difference is reduced to 2.0 °C, and the model can meet the requirements of the production. © TheMinerals, Metals & Materials Society 2025.

关键词： Continuous casting secondary cooling intelligent optimization algorithm Optimal modeling

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intelligent optimization algorithm grid computing-based applications

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JOURNAL OF intelligent & FUZZY SYSTEMS 2020年第4期39卷 5201-5211页

作者： Liu, Bingjie Zhu, Li Ren, Jianlan Jiangxi V&T Coll Commun Nanchang Jiangxi Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang Hubei Peoples R China

optimization algorithms have been rapidly promoted and applied in many engineering fields, such as system control, artificial intelligence, pattern recognition, computer engineering, etc.;achieving optimization in the production process has an important role in improving production efficiency and efficiency and saving resources. At the same time, the theoretical research of optimization methods also plays an important role in improving the performance of the algorithm, widening the application field of the algorithm, and improving the algorithm system. Based on the above background, the purpose of this paper is to apply the intelligent optimization algorithm based on grid technology platform to research. This article first briefly introduced the grid computing platform and optimization algorithms;then, through the two application examples of the TSP problem and the Hammerstein model recognition problem, the common intelligent optimization algorithms are introduced in detail. Introduction: algorithm description, algorithm implementation, case analysis, algorithm evaluation and algorithm improvement. This paper also applies the GDE algorithm to solve the reactive power optimization problems of the IEEE14 node, IEEE30 node and IEEE57 node. The experimental results show that the minimum network loss of the three systems obtained by the GDE algorithm is 12.348161, 16.348152, and 23.645213, indicating that the GDE algorithm is an effective algorithm for solving the reactive power optimization problem of power systems.

关键词： Grid computing platform intelligent optimization algorithm TSP problem hammerstein model simulated annealing algorithm

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intelligent optimization algorithm-driven informational teaching model for English reading and writing in universities

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第2期24卷 715-730页

作者： Liu, Peijun West Anhui Hlth Vocat Coll Dept Gen Educ Foreign Language Teaching & Res Sect Luan Anhui Peoples R China

English reading and writing are important parts of language teaching. In order to improve the English reading and writing ability of college students, the TLBO (teaching learning-based optimization) algorithm is used in this research to improve the way that English reading and writing are taught in colleges and universities. It is chosen as the primary model for this study. The TLBO algorithm is further optimized in this paper, and a convergence analysis is performed between the optimized model M-TLBO (multi-learning teaching learning-based optimization) algorithm and other TLBO algorithms in order to address the issues that the TLBO algorithm has an excessively single teaching ability and readily settles into local optimal solutions for some large-scale complex problems. In terms of stability and convergence accuracy, M-TLBO outperforms other algorithms. In order to investigate the impact of the M-TLBO algorithm on students' writing performance, this paper uses the teaching-learning optimization algorithm to conduct a pre-and post-test on students' English reading and writing performance in five dimensions. The study's findings revealed that students' pre-test writing scores had a mean value of 8.4770 and a standard deviation of 1.72449, and that their post-study writing scores had increased by 5.05 points. The English reading and writing information-based teaching model can improve students' English writing performance. It is hoped to promote the development of English teaching and improve the efficiency of students' English learning.

关键词： intelligent optimization algorithm TLBO algorithm M-TLBO model information-based teaching English reading and writing teaching model

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IMATSA - an improved and adaptive intelligent optimization algorithm based on tunicate swarm algorithm

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AI COMMUNICATIONS 2024年第1期37卷 1-22页

作者： Chen, Yan Dong, Weizhen Hu, Xiaochun Guangxi Univ Sch Business Adm Nanning 530000 Peoples R China Guangxi Univ Sch Comp & Elect Informat Sci Guangxi 530004 Peoples R China Guangxi Univ Finance & Econ Coll Big Data & Artificial Intelligence Guangxi 530007 Peoples R China Guangxi Key Lab Finance & Econ Big Data Nanning Peoples R China

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions;each component of IMATSA has a positive effect on the performance;IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT;there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test;in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance.

关键词： intelligent optimization algorithm optimization tent map quadratic interpolation Golden-Sine levy flight improved Gauss disturbance

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intelligent optimization algorithm for Support Vector Machine: Research and Analysis of Prediction Ability

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2024年第1期33卷 2350048-2350048页

作者： Lian, Lian Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110142 Peoples R China

Support vector machine is a very classical and popular model for data prediction. Traditional support vector machines use grid search to determine its parameters. In order to improve the accuracy of prediction, more and more frameworks are proposed. Among them, the combination of support vector machine and intelligent optimization algorithm is the most commonly used solution at present. The optimization objective is to determine the optimal penalty factor and kernel parameters of support vector machine to improve the prediction performance. In this paper, 10 intelligent optimization algorithms that are widely used at present are used for the optimization research of support vector machine. The performance of these optimization algorithms in support vector machine parameter optimization is analyzed in detail. Short-term wind speed and network traffic are chosen as the research object, and detailed performance indicators are given to judge the advantages and disadvantages of these intelligent optimization algorithms in optimizing support vector machine performance. Finally, the performance indicators, optimization speed, running memory usage, optimization success rate of different optimized SVM models, and impact of data distribution are analyzed in detail, and some conclusions are drawn. For the parameters optimization of support vector machine, various indicators are comprehensively considered, grey wolf optimizer algorithm and squirrel search algorithm are recommended.

关键词： Support vector machine intelligent optimization algorithm parameters optimization regression prediction

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intelligent optimization algorithm for the locating and Sizing of Distributed Generation Planning

Intelligent Optimization Algorithm for the locating and Sizi...

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2010年中国国际供电会议(CICED 2010)

作者： Bo Liu Zhiqiang Yuan Hailong Bao Shanghai Electric Power Design Institute Co. LtdShanghai 200025China Institute of Technology & Management Shanghai Municipal Electric Power CompanyShanghai 200025China

ISBN: (纸本)9781457700668

This paper analyses the influence of network loss when distribution generation (DG) access to distribution network,and presents an intelligent optimization algorithm to optimize the locating and sizing of DG based on modified particle swarm optimal (PSO) algorithm. The purposed algorithm is to find how much distribution network loss can be further reduced. With an example the superiority of the proposed algorithm is demonstrated in comparison with the genetic algorithms. The calculation results show that the proposed algorithm to solve the problem of distributed generation planning has strong global search ability and rapid convergence speed.

关键词： Distribution generation distribution network locating and Sizing intelligent optimization algorithm

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Analysis of Power Load Forecasting Model Based on intelligent optimization algorithm 5

Analysis of Power Load Forecasting Model Based on Intelligen...

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Jiao, Jianli Shanghai Univ Med & Hlth Sci Shanghai 201318 Peoples R China

ISBN: (纸本)9781665426428

In recent years, the complex relationship between power supply and demand and economic growth has caused governments, enterprises and a large number of scientific researchers to pay attention to the internal relationship of power supply and demand forecasting. Power load forecasting has become a management task, science and power science, computer science and other fields. Research hotspots. This paper constructs a mathematical model and application prototype system based on intelligent optimization algorithms, predicts short-term and mid-to-long-term power load trends in my country, and analyzes the supply and demand situation of the power industry.

关键词： Power Load Forecasting Support Vector Machines intelligent optimization algorithm Forecasting System

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Optimal Synchronization in Dynamically Coupled Fractional-order Chaotic Systems Based on intelligent optimization algorithm with Simulink 35

Optimal Synchronization in Dynamically Coupled Fractional-or...

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35th Chinese Control and Decision Conference (CCDC)

作者： Xi, Huiling Zhang, Ruixia North Univ China Sch Math Taiyuan 030051 Shanxi Peoples R China

ISBN: (纸本)9798350334722

In this paper, for the synchronization control of the dynamically coupled fractional-order Rossler system, by reasonably selecting the optimization objective function and using the efficient global optimization ability of the intelligent optimization algorithm, the optimal values of the error system parameters are directly determined;besides, a fractional integral optimal sliding mode control laws is designed, so that the parameters are changed from unknown to known, which avoids the construction of Lyapunov function according to the Lyapunov stability principle and the large amount of calculation of trial and error method, and provides an idea for us to determine the parameters in the system or controller. With the help of MATLAB Simulink, the fractional differential solver is obtained and the Simulink block diagrams of the systems is made. The fast synchronization of the dynamically coupled drive and response system can be achieved by substituting the optimal parameter value into the systems. The numerical simulation results verify the feasibility and effectiveness of the proposed method.

关键词： Dynamically coupled fractional-order chaotic system intelligent optimization algorithm Matlab Simulink Optimal synchronization Sliding mode control

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Research on an intelligent optimization algorithm for Combinatorial optimization Problem Based on Big Data

Research on an Intelligent Optimization Algorithm for Combin...

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International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Zhang, Xuecong Jinan Univ Dept Math Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728196190

In this era of rapid development, the development of a multi-level capital market is an urgent social concern. In the new era, new requirements are put forward for the study of portfolio optimization model in China. Under the background of big data era, if we want to further reduce and disperse the risk of financial investment market and increase the universality of portfolio investment optimization field in China, we must correctly deal with the impact and challenge brought by the era, combine with the new requirements of the new era, carry out reform and innovation on portfolio optimization, so as to promote its development. In order to better analyze the development progress of portfolio optimization problem;this paper puts forward a research method of intelligent optimization algorithm which applies big data technology to portfolio optimization problem, so as to put forward a set of new scheme of portfolio optimization problem which fully meets the development requirements of the new era. Through long-term research and analysis, it is found that the research scheme proposed in this paper successfully provides a new idea for the research method of intelligent optimization algorithm based on big data for combinatorial optimization problems.

关键词： Capital Market Portfolio optimization Big Data Technology intelligent optimization algorithm

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An optimization Method for Optimal Engineering Structure Design Combining intelligent optimization algorithm and CAE Code

An Optimization Method for Optimal Engineering Structure Des...

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International Conference on Chemical, Material and Metallurgical Engineering (ICCMME 2011)

作者： Peng, Wenjie Ge, Rui Gu, Mingkai Wuhan Iron & Steel Grp Corp Ctr Res & Dev Wuhan 430080 Peoples R China Ningbo Entry Exist Inspect & Quarantine Bur Light Ind Inspect Ctr Ningbo 315000 Peoples R China

ISBN: (纸本)9783037853092

This paper presents an optimization method for optimal engineering structure design. An interface procedure is essentially developed to combine the intelligent optimization algorithm and computer aided engineering (CAE) code. An optimization example is carried out to minimize the interlaminar normal stress of a laminate which affect the delamination failure of a laminate via arranging the stacking sequence. The analytical solution is calculated to validate the accuracy of optimization results.

关键词： intelligent optimization algorithm CAE code Engineering structure design

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