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检索条件"机构=Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing"

共 22 条记录，以下是11-20 订阅

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Locally Linear Embedding Based on Neiderreit Sequence Initialized Ali Baba and The Forty Thieves Algorithm 4

Locally Linear Embedding Based on Neiderreit Sequence Initia...

引用

4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Li, Xinyu Zhu, Zhengdong Hui, Lijin Ma, Xiongfeng Li, Dan Yang, Zan Nai, Wei Huzhou College School of Economics and Management Huzhou China Huzhou College Public Teaching and Research Department Huzhou China Huzhou College School of Electronic Information Huzhou China Huzhou College Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China

ISBN: (纸本)9798350363586

Manifold learning is a nonlinear dimensionality reduction technique that reveals the essential features and structure of data through dimensionality reduction. This technique has enormous theoretical research and industry application value in fields such as data visualization, denoising, and anomaly detection. Locally linear embedding (LLE) is a classic algorithm in manifold learning, which projects high-dimensional data into a low-dimensional space while maintaining the same algebraic structure. However, the optimization objective function of LLE uses L2 norm to measure linear approximation error, which can easily amplify and reduce the error. Therefore, in this paper, L1 norm is used instead of L2 norm to overcome this deficiency, but it also brings about the problem of non-smoothness in the optimization objective function. To address this issue, in this paper, a derivative-free optimization method called Neiderreit sequence initialize Ali Baba and the forty thieves (NSAFT) algorithm has been proposed, and demonstrates its effectiveness in finding the optimal solution for the objective function of LLE through numerical experiments. © 2024 IEEE.

关键词： Dimensionality reduction

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t-SNE Based on Halton Sequence Initialized Mountaineering Team-Based Optimization Algorithm 14

t-SNE Based on Halton Sequence Initialized Mountaineering Te...

引用

14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Tang, Yue Nai, Wei Lei, Nengwei Li, Dan Yang, Zan School of Electronic Information Huzhou College Zhejiang Province Huzhou313000 China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Zhejiang Province Huzhou313000 China Public Teaching and Research Department Huzhou College Zhejiang Province Huzhou313000 China

ISBN: (纸本)9798350361889

t-distributed stochastic neighbor embedding (t-SNE) is a classic manifold learning algorithm in the field of machine learning (ML), and it is a non-linear data dimensionality reduction method. Manifold learning generally constructs homeomorphic mappings from high-dimensional spaces to low dimensional spaces based on different mathematical ideas. The idea of t-SNE is to pursue the probability isomorphism of data distribution before and after projection from high-dimensional space to low dimensional space. Thus, its essence can be seen as solving the minimization problem of Kullback-Leibler (KL) divergence that measures the similarity of probability distribution before and after projection. t-SNE algorithm usually uses gradient dependent line search optimization methods to solve, but gradient dependent optimization methods are often prone to fall into the trap of local optimal solutions and unable to find the global one. To address this issue, in this paper, a non-derivative optimization method, which is called Halton sequence initiated mountaineering team-based optimization (HSMTBO) has been proposed to realize the parameter optimization of t-SNE, and its feasibility and advantages can be verified through numerical experiments. © 2024 IEEE.

关键词： Probability distributions

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Optimizing Charging Strategies for Electric Vehicles in Residential Areas Based on Neiderreit Sequence Initialized Tasmanian Devil Optimization 14

Optimizing Charging Strategies for Electric Vehicles in Resi...

引用

14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Li, Dan Yang, Zan Wang, Pengbo Wang, Sijia Nai, Wei Public Teaching and Research Department Huzhou College Zhejiang Province Huzhou313000 China School of Electronic Information Huzhou College Zhejiang Province Huzhou313000 China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Zhejiang Province Huzhou313000 China

ISBN: (纸本)9798350361889

Electric vehicles (EV) have attracted the attention of many consumers due to their energy-saving and environmentally friendly characteristics. However, as the number of EVs continues to increase, a large number of EVs, when randomly connected to the power distribution network through charging stations, will cause a large-scale uncertain electricity load burden on the power distribution network. Therefore, it is meaningful to study the proper way to manage the increasing charging behavior of EVs and achieve their orderly charging. In recent years, scholars have been particularly inclined to describe the optimization of EV charging strategies as a multi-objective optimization problem. However, a considerable number of EV charging strategy models have an objective function that is essentially L1 norm, which is a non-smooth optimization problem. In order to effectively optimize its parameters, in this paper, an algorithm that does not rely on gradient information for global optimization has been proposed, which is called Neiderreit sequence initiated tasmanian devil optimization (NSTDO). NSTDO can overcome the shortcomings of commonly used coordinate descent (CD) method and sub gradient descent (SubGD) method, which are prone to falling into the trap of local minima and unable to find the global optimal solution. Via numerical experiment, the effectiveness of the proposed NSTDO algorithm has been verified. © 2024 IEEE.

关键词： Global optimization

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Metamorphic relationship generation based on Faure Sequence Initialized Slime Mould Algorithm 14

Metamorphic relationship generation based on Faure Sequence ...

引用

14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Hong, Tingzhi Qian, Leiyan Fang, Jiayi Wang, Tongshuai Zhu, Zhicheng Qin, Xin Nai, Wei Yang, Zan School of Electronic Information Huzhou College Zhejiang Province Huzhou313000 China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Zhejiang Province Huzhou313000 China Pablic Teaching and Research Department Huzhou College Zhejiang Province Huzhou313000 China

ISBN: (纸本)9798350361889

Metamorphic relationship is an extremely important software testing method in the field of software engineering, and after years of research by scholars, a relatively complete system has been formed. However, the objective function of the metamorphic relationship generation algorithm is essentially a non-differentiable L1 norm, which means that the algorithm can finally be transformed into a non-smooth optimization problem. If traditional gradient dependent methods like gradient descent (GD) or stochastic gradient descent (SGD) are used, the objective function cannot be solved. The coordinate descent (CD) or sub-gradient descent (SubGD) methods usually used to solve the objective function are prone to falling into the trap of local minima, making it difficult to find the global optimal solution. Therefore, it is necessary to find a derivative-free optimization method to solve the objective function of metamorphic relationship generation algorithm. In this paper, a novel slime mold algorithm (SMA), which is called the Faure sequence initialized SMA (FSSMA) has been proposed for global optimization of the objective function. FSSMA can effectively overcome the shortcomings of CD and SubGD methods. Through numerical experiments, the effectiveness and computational efficiency of FSSMA have been demonstrated. © 2024 IEEE.

关键词： Global optimization

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Metamorphic relationship generation based on Faure Sequence Initialized Slime Mould Algorithm

Metamorphic relationship generation based on Faure Sequence ...

引用

International Conference on Electronics Information and Emergency Communication

作者： Tingzhi Hong Leiyan Qian Jiayi Fang Tongshuai Wang Zhicheng Zhu Xin Qin Wei Nai Zan Yang School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Pablic Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

关键词： Software testing Search methods Software algorithms Optimization methods Linear programming Computational efficiency Software engineering

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Logistic Regression Based on Faure Sequence Initialized Fruit Fly Optimization Algorithm

Logistic Regression Based on Faure Sequence Initialized Frui...

引用

International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Xinsheng Sun Yingjie Wang Zan Yang Wei Nai Dan Li School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China Department of Public Education Huzhou College Huzhou China

As a regression algorithm, logistic regression can be used for not only regression prediction, but also classification. By employing Sigmod function of nonlinear transformation to obtain a value within (0, 1), it can be given the characteristic of probability in logistic regression. According to the rule “value is 1 when $p > 0.5$ , and value is 0 when $p < 0.5$ ”, logistic regression can be recognized as a 2-class classification problem and is highly explanatory. However, in the process of solving the parameters of the loss function of traditional logistic regression, gradient descent (GD) method is often used, which means that there is a requirement for the convexity of the function, and it is necessary to reasonably control the size of the hyperparameter in GD, otherwise it is easy to fall into the local optimum value and cannot obtain the global optimal solution. As new methods need to be found to solve the problem of loss function optimization, in this paper, a Faure sequence initialized fruit fly optimization algorithm (FSFFOA) method has been proposed to replace GD method to complete the optimization process. As a swarm intelligence algorithm, FSFFOA has many parameters that can be used to adjust the algorithm accuracy and speed, and has good performance in solving nonlinear optimization problems. Via numerical experiment, the effectiveness of FSFFA has been proved.

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Index t-SNE Based on Sobol Sequence Initialized Harris Hawk Optimization Algorithm

Index t-SNE Based on Sobol Sequence Initialized Harris Hawk ...

引用

International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yuanzhao Zhang Linsong Wei Wei Nai Dan Li Zan Yang School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China Department of Public Education Huzhou College Huzhou China

It is of no doubt that artificial intelligence (AI) is a cutting-edge technological science, in which manifold learning methods have become a hot research topic in recent years. t-distribution stochastic neighbor embedding (t-SNE) is a representative nonlinear dimensionality reduction method in manifold learning. The 2 main advantages of t-SNE are its ability to maintain structural characteristics and solve congestion problems. However, t-SNE is not parametric, so the 2 initializations of the algorithm will result in 2 different embeddings, thus, a guiding index is added to t-SNE, and thus index t-SNE algorithm has been formed. Nevertheless, like traditional t-SNE, the optimization method of index t-SNE is still prone to fall into the trap of local minimum, resulting in no global optimal solution. And the closer to the global optimal solution, the more oscillatory the parameter optimization path will be, which means that the optimization efficiency of index t-SNE is not high. The root of causing the above problems is that the optimization method of index t-SNE relies on gradient or derivative information. In this paper, a derivative-free swarm intelligence method - Sobol sequence initialized Harris hawk optimization (HHO) has been utilized to overcome the above shortcomings, and the advantages of the proposed method have been proved via numerical experiments.

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Optimizing Charging Strategies for Electric Vehicles in Residential Areas Based on Neiderreit Sequence Initialized Tasmanian Devil Optimization

Optimizing Charging Strategies for Electric Vehicles in Resi...

引用

International Conference on Electronics Information and Emergency Communication

作者： Dan Li Zan Yang Pengbo Wang Sijia Wang Wei Nai Public Teaching and Research Department Huzhou College Huzhou China School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

关键词： Electricity Charging stations Linear programming Electric vehicle charging Power systems Optimization Convergence

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t-SNE Based on Halton Sequence Initialized Mountaineering Team-Based Optimization Algorithm

t-SNE Based on Halton Sequence Initialized Mountaineering Te...

引用

International Conference on Electronics Information and Emergency Communication

作者： Yue Tang Wei Nai Nengwei Lei Dan Li Zan Yang School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Public Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

关键词： Machine learning algorithms Optimization methods Search problems Minimization Probability distribution Manifold learning Convergence

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Index t-SNE Based on Neiderreit Sequence Initialized White Shark Optimizer

Index t-SNE Based on Neiderreit Sequence Initialized White S...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Nengwei Lei Yue Tang Zan Yang Dan Li Wei Nai School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China Public Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

t-distributed stochastic neighbor embedding (t-SNE) is an unsupervised manifold learning method in the field of machine learning (ML). It mainly achieves noise removal and feature extraction through nonlinear data dimensionality reduction. However, t-SNE is a type of non parametric algorithm, the two initialization steps in its algorithm process may result in two different embeddings, leading to a decrease in computational efficiency. Index t-SNE method can solve the problem of t-SNE to some extent, but its loss function still relies on gradient line search methods to solve, such as gradient descent (GD) method and stochastic gradient descent (SGD) method which are prone to fall into the trap of local optima and cannot achieve global optimization. To overcome this drawback, in this paper, a derivative-free optimization method, which is called Neiderreit sequence initialized white shark optimizer (NSWSO) has been proposed. Via numerical experiment, its effectiveness and superiority comparing with gradient dependent methods are verified.

关键词： Manifolds Machine learning algorithms Search methods Noise Optimization methods Sharks Feature extraction

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