A Biological Immune Mechanism-Based Quantum PSO Algorithm and Its Application in Back Analysis for Seepage Parameters

Tan, Jiacheng; Xu, Liqun; Zhang, Kailai; Yang, Chenghu

doi:10.1155/2020/2191079

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…A supervised learning algorithm is used for the center and output weights of the RBF NN. It can improve the generalization performance of RBF NN [ 19 ]. This assumes that the network output is one-dimensional, using the following sum-of-squares cost function:

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Section: Optimization Methods Of Intelligent Education Based On Parti...mentioning

confidence: 99%

PSO Algorithm-Based Design of Intelligent Education Personalization System

Yang

2022

Computational Intelligence and Neuroscience

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The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.

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…”

Section: Optimization Methods Of Intelligent Education Based On Parti...mentioning

confidence: 99%

PSO Algorithm-Based Design of Intelligent Education Personalization System

Yang

2022

Computational Intelligence and Neuroscience

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“…One of the most important parameters in seepage calculations is the hydraulic conductivity [7]. Currently, there are three methods to determine hydraulic conductivity in hydraulic engineering including the test method, empirical formula method, and back analysis method [8]. The test method can in principle accurately obtain hydraulic conductivity based on in situ sampling.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression

Li,

Shen,

Sun

et al. 2023

Applied Sciences

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Seepage is the primary cause of dam failures. Conducting regular seepage analysis for dams can effectively prevent accidents from occurring. Accurate and rapid determination of seepage parameters is a prerequisite for seepage calculation in hydraulic engineering. The Whale Optimization Algorithm (WOA) was combined with Support Vector Regression (SVR) to invert the hydraulic conductivity. The good point set initialization method, a cosine-based nonlinear convergence factor, the Levy flight strategy, and the Quasi-oppositional learning strategy were employed to improve WOA. The effectiveness and practicality of Improved Whale Optimization Algorithm (IWOA) were evaluated via numerical experiments. As a case study, the seepage parameters of the Dono Dam located on the Baishui River in China were inversed, adopting the proposed inversion model. The calculated seepage field was reasonable, and the relative error between the simulated head and the measured value at each monitoring point was within 2%. This new inversion method is more feasible and accurate than the existing hydraulic conductivity estimation methods.

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“…Significantly, much progress has been made in the research field of seepage because of optimization algorithms [18][19][20][21][22][23][24][25]. Tan et al [26] proposed a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm to solve the inversion problem of seepage parameters. Based on back propagation neural network (BPNN) and genetic algorithm (GA), Zhou et al [27] developed a new approach for inverse modeling of the transient groundwater flow in dam foundations, which improved the uniqueness and reliability of the inversed results and made tractable the large-scale inverse problems in engineering practices.…”

Section: Introductionmentioning

confidence: 99%

Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer

Shu

Shen

et al. 2022

Applied Sciences

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The seepage parameters of the dam body and dam foundation are difficult to determine accurately and quickly. Based on the inverse analysis, a Gray Wolf Optimizer (GWO) was introduced into this study to search the target hydraulic conductivity. A novel approach for initialization, a polynomial-based nonlinear convergence factor, and weighting factors based on Euclidean norms and hierarchy were applied to improve GWO. The practicability and effectiveness of Improved Gray Wolf Optimizer (IGWO) were evaluated by numerical experiments. Taking Kakiwa dam located on the Muli River of China as a case, an inversion analysis for seepage parameters was accomplished by adopting the proposed optimization algorithm. The simulated hydraulic heads and seepage volume agree with measurements obtained from piezometers and measuring weir. The steady seepage field of the dam was analyzed. The results indicate the feasibility of IGWO in determining the seepage parameters of Kakiwa dam.

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A Biological Immune Mechanism-Based Quantum PSO Algorithm and Its Application in Back Analysis for Seepage Parameters

Cited by 7 publications

References 31 publications

PSO Algorithm-Based Design of Intelligent Education Personalization System

PSO Algorithm-Based Design of Intelligent Education Personalization System

A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression

Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer

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