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A Reservoir Dam Monitoring Technology Integrating Improved ABC a*algorithm and SVM a*algorithm

WATER 2025年第3期17卷 302-302页

作者： Xu, Yunqian Bao, Tengfei Yuan, Mingdao Zhang, Shu Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210024 Peoples R China Guangdong Res Inst Water Resources & Hydropower Guangzhou 510635 Peoples R China State & Local Joint Engn Lab Estuarine Hydraul Tec Guangzhou 510635 Peoples R China

A reservoir dam is a water conservancy project with large investment and high social and economic benefits, which plays an irreplaceable role in flood control, power generation, water storage, and urban water supply. There is a risk of accidents in the process of reservoir dams, so dam monitoring is an important means to achieve the safe operation of reservoirs. In this paper, taking advantage of the high-dimensional and nonlinear characteristics of dam monitoring data samples, the fusion-improved ABC (artificial bee colony) a*algorithm is introduced, and the SVM (support vector machine) a*algorithm is used to optimize the penalty factor and kernel function parameters. The test results of the ABC and SVM a*algorithm are relatively stable, with small fluctuation amplitude, which can continuously monitor water level, pore water pressure, dam deformation, temperature, humidity, vibration, and other indicators is less than 10%, which is significantly lower than the standard ABC a*algorithm, the standard ANN a*algorithm, and the standard SVM a*algorithm. The independence and characteristics of the ABC-SVM a*algorithm are significantly higher, and the correlation is 0.03, the RMS (root mean square) is 0.2334, which is lower than that of the standard ABC a*algorithm of 0.09, and the standard ANN a*algorithm of 0.8. The stability of the results and performance stability are analyzed, which is greater than 90%. The ABC and SVM is used to predict the displacement and deformation law of the reservoir dam.

关键词： reservoir dam artificial bee colony a*algorithm support vector machines dam deformation monitoring

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APPROXIMATION a*algorithm FOR TWO-STAGE STOCHASTIC FAULT-TOLERANT FACILITY LOCATION PROBLEM

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第3期21卷 1735-1748页

作者： Zhang, Li Li, Qiaoliang Hunan Agr Univ Coll Informat & Intelligence Changsha 410128 Hunan Peoples R China Hunan Normal Univ Sch Math & Stat Changsha 410081 Hunan Peoples R China

In this paper, we consider the two-stage stochastic fault-tolerant facility location problem with uniform connectivity demand (2-SFTFLPUCD). We present the currently best known approximation ratio 1.8526 for 2-SFTFLPUCD. Initially, we demonstrate a stronger result of the 3-approximation a*algorithm based on analysis of the sum of opening and connection cost. Subsequently, we seed a greedy augmentation a*algorithm with the solution of a 3-approximation a*algorithm to yield an improved approximation ratio.

关键词： Stochastic fault-tolerant approximation a*algorithm greedy augmentation

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A flexible and efficient a*algorithm for high dimensional support vector regression

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NEUROCOMPUTING 2025年 611卷

作者： Yang, Menglei Liang, Hao Wu, Xiaofei Zhang, Zhimin Chongqing Univ Coll Math & Stat Chongqing 401331 Peoples R China Shandong Univ Cheeloo Coll Med Sch Publ Hlth Dept Microbiol Jinan 250012 Peoples R China

In high dimensional statistical learning, variable selection and handling highly correlated phenomena are two crucial topics. Elastic-net regularization can automatically perform variable selection and tends to either simultaneously select or remove highly correlated variables. Consequently, it has been widely applied in machine learning. In this paper, we incorporate elastic-net regularization into the support vector regression model, introducing the Elastic-net Support Vector Regression (En-SVR) model. Due to the inclusion of elastic- net regularization, the En-SVR model possesses the capability of variable selection, addressing high dimensional and highly correlated statistical learning problems. However, the optimization problem for the En-SVR model is rather complex, and common methods for solving the En-SVR model are challenging. Nevertheless, we observe that the optimization problem for the En-SVR model can be reformulated as a convex optimization problem where the objective function is separable into multiple blocks and connected by an inequality constraint. Therefore, we employ a novel and efficient Alternating Direction Method of Multipliers (ADMM) a*algorithm to solve the En-SVR model, and provide a complexity analysis as well as convergence analysis for the a*algorithm. Furthermore, extensive numerical experiments validate the outstanding performance of the En-SVR model in high dimensional statistical learning and the efficiency of this novel ADMM a*algorithm.

关键词： Support vector regression Elastic-net regularization ADMM a*algorithm Variable selection High dimensional statistical learning

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A novel arctic fox survival strategy inspired optimization a*algorithm

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2025年第1期49卷 1-73页

作者： Subha, E. Jothi Prakash, V. Antran Vijay, S. Arul Karpagam Coll Engn Coimbatore 641032 Tamil Nadu India

In the field of optimization a*algorithms, nature-inspired techniques have garnered attention for their adaptability and problem-solving prowess. This research introduces the Arctic Fox a*algorithm (AFA), an innovative optimization technique inspired by the adaptive survival strategies of the Arctic fox, designed to excel in dynamic and complex optimization landscapes. Incorporating gradient flow dynamics, stochastic differential equations, and probability distributions, AFA is equipped to adjust its search strategies dynamically, enhancing both exploration and exploitation capabilities. Through rigorous evaluation on a set of 25 benchmark functions, AFA consistently outperformed established a*algorithms particularly in scenarios involving high-dimensional and multi-modal problems, demonstrating faster convergence and improved solution quality. Application of AFA to real-world problems, including wind farm layout optimization and financial portfolio optimization, highlighted its ability to increase energy outputs by up to 15% and improve portfolio Sharpe ratios by 20% compared to conventional methods. These results showcase AFA's potential as a robust tool for complex optimization tasks, paving the way for future research focused on refining its adaptive mechanisms and exploring broader applications.

关键词： Arctic fox a*algorithm Optimization techniques Dynamic environments Bio-inspired a*algorithms Multi modal optimization

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Optimal Design of Water Transmission Pipelines Using a Metaheuristic a*algorithm and Geoprocessing Techniques

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WATER RESOURCES MANAGEMENT 2025年 1-16页

作者： Duarte, Armando Dias Cirilo, Jose Almir Vasconcelos, Rochele Sheila Tahimi, Abdeladhim Sarmento, Francisco Jacome Bezerra, Saulo de Tarso Marques Univ Fed Oeste Bahia Dept Prod Engn Luiz Eduardo Magalhaes BA Brazil Univ Fed Pernambuco Dept Technol Ave Marielle FrancoS-NBairro Nova Caruaru BR-55014593 Caruaru PE Brazil Univ Estadual Santa Cruz Dept Agr Sci & Environm Ilheus BA Brazil Univ Fed Alagoas Dept Math Maceio AL Brazil Univ Fed Paraiba Dept Civil & Environm Engn Joao Pessoa PB Brazil

Traditionally, the various components of water supply systems have been designed using trial-and-error techniques guided by designers' experience. While these conventional methods can be effective in certain cases, they often lead to suboptimal solutions. Advances in computational modeling have enabled the development of more accurate and cost-effective solutions by integrating geospatial data, hydraulic constraints, and economic factors into the decision-making process. This study proposes a metaheuristic technique for optimizing pipeline routes, enhancing the traditional A-Star a*algorithm. The developed method, named Modified A-Star for Pipeline Routing (MAPR), operates within a specific search space, considering key variables that influence the determination of the optimal route. MAPR minimizes total costs associated with construction and energy consumption by incorporating a multiplier coefficient that adjusts the relative importance of different trajectories. This adjustment accelerates convergence to the water delivery point while accounting for cost variations. Simulation results demonstrate MAPR's effectiveness in generating cost-efficient layouts across different terrains and pipeline configurations. Notably, the best simulation results were achieved when the Destiny Way coefficient, which balances the importance of displacement toward the destination against the costs, was set to 1.0. At this value, an equilibrium is achieved, ensuring that no cost component is disproportionately prioritized. MAPR proves to be a valuable tool for the automatic determination of optimal pipeline routes, reducing subjectivity in design while minimizing both implementation and operational costs. These characteristics make it a promising approach for the design of real-world water supply systems.

关键词： Water supply Optimal pipelines routes A-Star a*algorithm Optimization

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A computationally efficient multichannel feedforward time-frequency-domain adjoint least mean square a*algorithm for active road noise control

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APPLIED ACOUSTICS 2025年 231卷

作者： Li, Xiaolong Lu, Chihua Chen, Wan Zhu, Yawei Liu, Zhien Cheng, Can Sun, Menglei Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Peoples R China Foshan Xianhu Lab Adv Energy Sci & Technol Guangdo Foshan 528200 Peoples R China Dongfeng Motor Corp Tech Wuhan 430058 Peoples R China

Active road noise control (ARNC) emerges as a promising solution for reducing vehicle interior road noise, owing to its efficacy in controlling low-frequency noise. However, the feedforward ARNC system demands numerous reference signals to achieve notable control, leading to a significant computational burden. To address this, the adjoint least mean square (ALMS) a*algorithm might be a viable alternative due to its lower computational intensity, especially in multichannel scenarios. Nevertheless, factors such as the spectral distribution of reference signals and complex acoustic paths with multi-modal responses may impact its convergence performance. This paper introduces a time-frequency-domain ALMS (TFD-ALMS) a*algorithm, where weight vectors are updated in the frequency domain, allowing for individualized step size settings for each frequency point, thereby enhancing convergence performance. Additionally, the time-reversed estimated secondary paths with magnitude equalization are constructed to mitigate the adverse impact of the magnitude-frequency response of the acoustic paths. Simulations are conducted to assess the performance of the TFD-ALMS a*algorithm, accompanied by discussions on the potential for the a*algorithm to converge to a biased solution due to the proposed step size setting. Moreover, the influence of the reference signal delay on the convergence speed is examined in a broader context. Consequently, a strategy for determining the fastest convergence step size, based on the Fibonacci search method, is proposed to aid in studying the convergence performance of the a*algorithm when dealing with narrowband and broadband noises of varying frequencies or bandwidths. Finally, the proposed a*algorithm is validated through ARNC tests under steady-state and unsteady-state working conditions. Results illustrate its capability to achieve superior control performance with reduced computational complexity compared to conventional methods.

关键词： Multichannel active noise control Adjoint least mean square a*algorithm Time-frequency-domain Road noise control Fibonacci search method

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An accelerated first-order regularized momentum descent ascent a*algorithm for stochastic nonconvex-concave minimax problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2025年第2期90卷 557-582页

作者： Zhang, Huiling Xu, Zi Shanghai Univ Coll Sci Dept Math Shanghai 200444 Peoples R China Shanghai Univ Newtouch Ctr Math Shanghai 200444 Peoples R China

Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose an accelerated first-order regularized momentum descent ascent a*algorithm (FORMDA) for solving stochastic nonconvex-concave minimax problems. The iteration complexity of the a*algorithm is proved to be (O) over tilde(epsilon(-6.5)) to obtain an epsilon-stationary point, which achieves the best-known complexity bound for single-loop a*algorithms to solve the stochastic nonconvex-concave minimax problems under the stationarity of the objective function.

关键词： Stochastic nonconvex minimax problem Accelerated momentum projection gradient a*algorithm Iteration complexity Machine learning

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Multi-strategy ant colony optimization with k-means clustering a*algorithm for capacitated vehicle routing problem

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-24页

作者： Zhang, Zhaojun Tan, Simeng Qin, Jiale Zou, Kuansheng Zhou, Shengwu Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Land Surveying & Mapping Inst Jinan 250102 Shandong Peoples R China

The capacitated vehicle routing problem (CVRP) is a well-known optimization issue in transportation logistics. As a typical representative of swarm intelligence a*algorithm, ant colony optimization (ACO) has shown encouraging outcomes in CVRP. In contrast, ACO has limitations such as undesirable solutions and susceptibility to getting stuck in local optima. To address these challenges, a multi-strategy adaptive ant colony optimization with the k-means clustering a*algorithm (KMACO) is proposed for solving CVRP in this study. In the initial stage of KMACO, k-means clustering a*algorithm is introduced to enhance the quality of the initial solution. Simultaneously, a path-saving factor is added to the state transition rules to improve the success rate of planning. Moreover, the a*algorithm's global search capability is further enhanced by dynamically adjusting the pheromone volatilization coefficient. Then, a problem-specific crossover operator and three-stage local operators are designed to strike a balance between the global optimization and local search of KMACO. Finally, to confirm the effectiveness of KMACO, simulation experiments are conducted on three types of datasets. Compared with ACO and six other intelligent a*algorithms, the KMACO achieves the best-known solution in 17, 12, and 10 instances in benchmark sets A, B, and P, respectively.

关键词： Vehicle routing problem Ant colony optimization K-means clustering a*algorithm Three-stage local operators

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Pheromone-based graph embedding a*algorithm for Ethereum phishing detection

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COMPUTER NETWORKS 2025年 260卷

作者： Xiao, Siyi Zhang, Lejun Tian, Zhihong Su, Shen Qiu, Jing Guo, Ran Yangzhou Univ Coll Informat Engn Yangzhou 225127 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Minist Educ Res & Dev Ctr ELearning Beijing 100039 Peoples R China Quanzhou Normal Univ Quanzhou 362000 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Guangzhou Univ Sch Phys & Mat Sci Guangzhou 510006 Peoples R China

Phishing scams pose significant risks to Ethereum, the second-largest blockchain-based cryptocurrency platform. Traditional methods for identifying phishing activities, such as machine learning and network representation learning, struggle to capture the temporal and repetitive transaction patterns inherent in Ethereum's transaction network. To address these limitations, we propose a Pheromone-based Graph Embedding a*algorithm (PGEA), which leverages pheromone mechanisms and a taboo list inspired by ant colony behavior to enhance subgraph sampling. This approach improves the identification of phishing activities by ensuring subgraph homogeneity and isomorphism during the sampling process. In our methodology, Ethereum transaction data is collected from known phishing addresses to construct a transaction network graph. The PGEA guides subgraph sampling, producing sequences that are transformed into node embeddings using word2vec. These embeddings are then classified using a Support Vector Machine (SVM) to distinguish between legitimate and malicious nodes. Experimental results demonstrate the superiority of our model over existing methods. PGEA achieves an accuracy of 87.18%, precision of 91.01%, recall of 84.82%, and F1 score of 86.91%, outperforming baseline approaches such as Deepwalk, Node2vec, and Graph2vec. These results highlight the efficacy of PGEA in detecting phishing addresses, contributing to amore secure Ethereum ecosystem.

关键词： Phishing scams Representation learning Transaction network Ant colony a*algorithm Graph embedding

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A Local Search a*algorithm for the Radius-Constrained k-Median Problem

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THEORY OF COMPUTING SYSTEMS 2025年第1期69卷 1-18页

作者： Chi, Gaojie Guo, Longkun Jia, Chaoqi Guangdong Univ Technol Sch Integrated Circuits Guangzhou Peoples R China Fuzhou Univ Sch Math & Stat Fuzhou 350116 Peoples R China RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic 3000 Australia

Given a radius R is an element of Z+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R\in \mathbb {Z}<^>+$$\end{document} and a set X of n points distributed within a metric space, we consider the radius-constrained k-median problem, which combines both the k-center and k-median clustering problems. In this problem, the objective is the same as that of the k-median problem, with the additional constraint that every point x in X must be assigned to a center within the given radius R. This paper proposes an approximation a*algorithm that achieves a bicriteria approximation ratio of (3+epsilon,7)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(3+\varepsilon , 7)$$\end{document} by incorporating local search with a key ball structure. The a*algorithm constructs a keyball center set to ensure coverage of the points and iteratively refines the solution through subset swaps while satisfying feasibility conditions. Thus, this process maintains coverage while reducing costs. Compared to the state-of-the-art approximation ratio of (8, 4) based on a linear programming formulation for this problem, our approach improves the k-median ratio from 8 to 3+epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3+\varepsilon $$\end{document}, at the cost of increasing the radius ratio from 4 to 7.

关键词： Local search k-median k-center Approximation a*algorithm

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