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 ...
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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 optimizationalgorithm 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.
optimizationalgorithms 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...
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optimizationalgorithms 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 optimizationalgorithms;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.
Swarm intelligence optimizationalgorithm 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 ...
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Swarm intelligence optimizationalgorithm 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.
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 a...
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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 optimizationalgorithms 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.
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. U...
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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.
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 ...
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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.
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 relations...
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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.
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 (CA...
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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.
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 abilit...
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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.
With the continuous development of computer technology, computer image processing technology has become an indispensable part of modern society. Computer image processing technology mainly includes image acquisition, ...
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