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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Jingyang Xiao, Mingyu University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

The Traveling Tournament Problem (TTP-k) is a well-known benchmark problem in tournament timetabling. It involves designing a feasible double round-robin tournament for a sports league of n teams under several feasibility requirements, while minimizing the total traveling costs of the teams. The parameter k requires that in the tournament at most k consecutive home games or away games for each team are allowed. TTP-k with a small k, especially for k = 2, 3 and 4, have been extensively studied in the literature. In this paper, we focus on TTP-4 and design an efficient algorithm for it based on minimum weight matching. In theory, we prove that our algorithm has an approximation ratio of 1.625 + Ε for any constant Ε > 0, improving the best-known approximation ratio of 1.7 + Ε. In practice, our experimental results indicate an average improvement of 6.65% over the best-known solutions on 9 benchmark instances. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： approximation algorithms

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Control Barrier Function-based Predictive Control for Close Proximity Operation of UAVs Inside a Tunnel

Control Barrier Function-based Predictive Control for Close ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Vedant Mundheda Damodar Datta K Harikumar Kandath Robotics Institute Carnegie Mellon University Chalmers University of Technology Robotics Research Center IIIT-Hyderabad

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This study introduces a control strategy for Un-manned Aerial Vehicles (UAVs) performing high-precision proximity tasks in restricted tunnel environments, enabling them to conduct detailed inspections and navigate through extremely narrow tunnel corridors. The primary challenge in these tasks lies in managing nonlinear aerodynamic forces and torques induced by the tunnel walls while ensuring safe and efficient UAV operation in close proximity to these boundaries. To tackle this issue, we propose a novel approach that integrates Model Predictive Control (MPC) with modified Control Barrier Function (CBF) constraints. This framework is designed to achieve dual objectives: ensuring a safe operational distance from walls to mitigate their aerodynamic effects, while simultaneously minimizing distance to the walls to effectively perform close-proximity operations. Our approach demonstrates significant improvements, reducing the safe hovering distance from walls by 37% and decreasing UAV power consumption by approximately 15% when flying near ground and ceiling surfaces. The efficacy of the proposed method is rigorously validated through comprehensive simulations, which evaluate various close-proximity UAV trajectories under realistically modeled aerodynamic disturbances induced by the tunnel boundaries.

关键词： Power demand Predictive models Inspection Autonomous aerial vehicles Aerodynamics approximation algorithms Aircraft navigation Trajectory Safety Predictive control

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Timetable Rescheduling for High-Speed Trains Based on a Multi-Agent Game Reinforcement Learning Method

Timetable Rescheduling for High-Speed Trains Based on a Mult...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiaomin Zhou Min Zhou Junfeng Cui Haifeng Song Hairong Dong School of Automation and Intelligence Beijing Jiaotong University Beijing China China Railway Signal and Communication Research & Design Institute Group Co. Ltd. Beijing China School of Electronic and Information Engineering Beihang University Beijing China Shandong University of Science and Technology Qingdao China Beijing Jiaotong University Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

As the high-speed rail network grows, unexpected disruptions affecting train operations have become increasingly common. Consequently, efficiently adjusting train schedules to minimize delays has become a critical challenge in high-speed rail timetable rescheduling (TTR). To address this issue, this paper proposes a Multi-Agent Game Deep Reinforcement Learning (MAGDRL) approach to tackle the TTR problem during a complete blockage. Initially, the paper defines the MAGDRL state and action spaces, incorporating spatiotemporal distribution information, and develops an effective reward feedback system. Next, the Nash-Q learning algorithm ensures convergence to the Nash equilibrium strategy. Additionally, an approximate Nash equilibrium solving method is applied to enhance the computational efficiency of determining the Nash equilibrium strategy. Finally, simulation results based on the Beijing-Shanghai high-speed railway demonstrate that the proposed method effectively handles timetable rescheduling across multiple disruption scenarios involving complete blockages of segments.

关键词： Schedules Simulation Games Nash equilibrium approximation algorithms Rail transportation Spatiotemporal phenomena Computational efficiency Delays Convergence

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A Pruning Stochastic Configuration Network Algorithm Based on Node Similarity and Contribution and Its Application in Wastewater Treatment Processes

A Pruning Stochastic Configuration Network Algorithm Based o...

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Chinese Control and Decision Conference, CCDC

作者： Tenghui Geng Wei Chai Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Stochastic configuration networks (SCNs) have been extensively employed in the modeling of nonlinear system regression tasks. The SCN model stops training by setting the maximum number of nodes in the hidden layer or the expected error tolerance. If the parameters are not set properly, it is easy to cause the model to overfit. To address this problem, a pruning SCN algorithm based on node similarity and contribution (SCPSCN) is proposed. First of all, we introduce the definitions of node similarity. Then, basing on Garson algorithm, a method that can effectively judge the contribution of hidden layer nodes is introduced. Finally, the redundant and low-quality nodes are deleted based on the similarity and contribution so that the optimal network structure of SCN is obtained. The proposed method has been successfully applied to prediction of BOD in a wastewater treatment plant. The experimental results show that SCPSCN has better prediction performance than the original SCN method.

关键词： Training Neurons Stochastic processes Effluents Predictive models Prediction algorithms approximation algorithms

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Congestion Management in Power Transmission System of IEEE30 Bus network using PSO AND PSOGSA

Congestion Management in Power Transmission System of IEEE30...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Annu Dalal Sushil Kumar Gupta DCRUST Sonipat Haryana

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

In a deregulated power system congestion management is a major issue of concern. In respect of transmission line losses, optimal power flow , operating limits of a power network are the main research attentions in the restricted power network .In today’s scenario, transaction of power becomes more significant due to increase in demand of customers and it becomes challenging to meet the requirement. As a result increased stress on transmission system line flow limits may violates, threat to voltage security also increases the risk of power system stability. Hence, for continuous and secure power flow n the transmission lines it is more significant that the generated electrical energy is transmitted as well as distributed proficiently in order to fulfill power *** rescheduling the generators, this research paper introduces a new hybrid optimization algorithm named as PSOGSA for eliminating of congestion from the congested transmission lines. The exploitation and exploration phases are the two main processes that this metaheuristic optimization algorithm followed. The exploitation phase uses the PSO (Particle Swarm Optimization) method to determine the fitness value, and the GSA (Gravitational Search method) is utilized to evaluate the fitness new position. The suggested method results are compared with an unmanaged network, such as the IEEE 30 bus system. Compared to PSO, this suggested method, PSOGSA, offered a superior solution.

关键词： Power transmission lines Costs Voltage Power system stability approximation algorithms Hybrid power systems Generators Stress Standards Load flow

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A Fast and Energy-Efficient Method for Online and Incremental Pareto-Front Update

A Fast and Energy-Efficient Method for Online and Incrementa...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Paulo J. S. Ferreira João Mendes-Moreira João M. P. Cardoso INESC TEC Faculty of Engineering University of Porto Portugal

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Self-adaptive Systems (SaS) are becoming increasingly important for adapting to dynamic environments and for optimizing performance on resource-constrained devices. A practical approach to achieving self-adaptability involves using a Pareto-Front (PF) to store the system’s hyper-parameters and the outcomes of hyperparameter combinations. This paper proposes a novel method to approximate a PF, offering a configurable number of solutions that can be adapted to the device’s limitations. We conducted extensive experiments across various scenarios, where all PF solutions were replaced, and real world scenarios were performed using actual measurements from a Human Activity Recognition (HAR) system. Our results show that our method consistently outperforms previous methods, mainly when the maximum number of PF solutions is in the order of hundreds. The effectiveness of our method is most apparent in real-case scenarios where it achieves, when executed in a Raspberry Pi 5, up to 87% energy consumption reduction and lower execution times than the second-best algorithm. Additionally, our method ensures a more evenly distributed solution across the PF, preventing the high concentration of solutions.

关键词： Performance evaluation Energy consumption Memory management approximation algorithms Size measurement Dynamic scheduling Energy efficiency Human activity recognition Internet of Things

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Transmit Beamforming and User Selection for Multi-Static Integrated Sensing and Communications

Transmit Beamforming and User Selection for Multi-Static Int...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Dan Wang Yuanming Tian Chuan Huang Hao Chen Xiaodong Xu Department of of Broadband Communications Peng Cheng Laboratory Shenzhen China School of Science and Engineering Shenzhen Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing Department of Broadband Communication Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper studies a multi-static integrated sensing and communications (ISAC) system for multi-user downlink communications and cooperative target sensing, where the base station transmits ISAC signal and the users deployed at different positions receive it. We analyze the joint estimation of transmission delay and Doppler shift of cooperative users for target sensing, and derive the corresponding Cramér-Rao bound (CRB) in closed form. Then, a CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements among these users. To solve this problem, we propose a minimax linkage-based cooperative method for user selection, and then design an approximation non-convex transforming algorithm for transmit beamforming. Finally, simulation results validate our analysis and reveal significant performance improvement of our proposed methods over the state-of-the-art benchmarks.

关键词： Doppler shift Array signal processing Simulation Estimation Integrated sensing and communication Minimization approximation algorithms Downlink Delays Land mobile radio

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Design of Power Grid Flexible Planning Algorithm Based on Deep Learning

Design of Power Grid Flexible Planning Algorithm Based on De...

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Electrical Drives, Power Electronics & Engineering (EDPEE), International Conference on

作者： Ya Zuo Xiangping Li Xiaojun Zhang Gang Chen Weidong Zheng State Grid Xinjiang Electric Power Co LTd Urumqi Xinjiang China State Grid Urumqi Electric Poer Supply Company Urumqi Xinjing China State Grid Xinjiang Economic Research Institute Urumqi Xinjiang China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

Conventional methods usually only consider one possible future environment, and cannot deal with the influence of various uncertain factors, so the obtained flexible planning scheme of power grid has poor adaptability to external environmental changes. This paper mainly discusses the flexible planning algorithm of power grid affected by uncertain factors. In this paper, the global optimization ability of GA(genetic algorithm) and the nonlinear approximation ability of BPNN(BP neural network) are combined for analysis and improvement, and an algorithm model of power grid flexible planning based on deep learning is established. It can not only reduce the search space of GA and improve the search efficiency, but also easily converge to the optimal solution, which provides a new strategy for solving the flexible power grid planning problem. It is found that the cost of the control group without considering the flexible balance of supply and demand is much higher than the total cost of this model, which shows the economic advantages of this model. The flexible cost of wind power and the flexible cost of load respectively represent that the lower the cost of waste air volume and load shedding, the better the wind power absorption capacity and the stronger the power supply reliability. The obtained planning scheme is more flexible and adaptable in the uncertain environment in the future.

关键词： Deep learning Costs Supply and demand Atmospheric modeling Wind power generation approximation algorithms Power grids

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Sampling, Counting, and Large Deviations for Triangle-Free Graphs Near the Critical Density

Sampling, Counting, and Large Deviations for Triangle-Free G...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Matthew Jenssen Will Perkins Aditya Potukuchi Michael Simkin Department of Mathematics King's College London London UK School of Computer Science Georgia Tech Atlanta USA Department of EECS York University Toronto Canada Department of Mathematics MIT Cambridge USA

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

We study the following combinatorial counting and sampling problems: can we sample from the Erdős-Rényi random graph $G(n,p)$ conditioned on triangle-freeness? Can we approximate (either algorithmically or with a formula) the probability that $G(n,p)$ is triangle-free? These are prototypical instances of forbidden substructure problems ubiquitous in combinatorics. The algorithmic questions are instances of approximate sampling and counting for a hypergraph hard-core model. Estimating the probability that $G(n,p)$ has no triangles is a fundamental question in probabilistic combinatorics and one that has led to the development of many important tools in the field. Through the work of several authors, the asymnpotics of the logarithm of this probability are known if $p=o(n^{-1/2})$ or if $p=\omega(n^{-1/2})$ . The regime $p=\Theta(n^{-1/2})$ is more mysterious, as this range witnesses a dramatic change in the the typical structural properties of $G(n,p)$ conditioned on triangle-freeness. As we show, this change in structure has a profound impact on the performance of sampling algorithms. We give two different efficient sampling algorithms for this problem (and complementary approximate counting algorithms), one that is efficient when $p < c/\sqrt{n}$ and one that is efficient when $p > C/\sqrt{n}$ for constants $c, C > 0$ . The latter algorithm involves a new approach for dealing with large defects in the setting of sampling from low-temperature spin models. Our algorithmic results can be used to give an asymptotic formula for the logarithm of the probability $G(n,p)$ is triangle-free when $p < c/\sqrt{n}$ . This algorithmic approach to large deviation problems in random graphs is very different than the known approaches in the suBCRitical regime $p=o(n^{-1/2})$ (based on the Poisson paradigm) and in the supercritical regime $p=\omega(n^{-1/2})$ (based on regularity lemmas or hypergraph containers); in fact, to the b

关键词： Computer science Containers approximation algorithms Probabilistic logic

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Adaptive algorithms for Generalized Eigenvalue Decomposition with a Nonquadratic Criterion

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Chinese Journal of Electronics 2023年第4期22卷 807-813页

作者： WANG Rong GAO Feifei YAO Minli ZOU Hongxing HighTech Institute of Xi'an Xi'an China Department of Automation Tsinghua University Beijing China Tsinghua National Laboratory for Information Science and Technology Beijing China

In this paper, we propose a nonquadratic criterion to solve the Generalized eigenvalue decomposition (GED) problem. This criterion exhibits a single global maximum that is attained if and only if the weight matrix spans the principal generalized subspace. The other stationary points of this criterion are (unstable) saddle points. Since the criterion is nonquadratic, it has a steep landscape and, therefore, yields fast gradient-based algorithms. Applying the projection approximation method and Recursive least squares (RLS) technique, we develop an adaptive algorithm with low computational complexity to track the principal generalized subspace, as well as an adaptive algorithm to parallely estimate the principal generalized eigenvectors. Numerical results are provided to corroborate the proposed studies.

关键词： Simulation Adaptive algorithms Numerical simulation approximation algorithms Eigenvalues and eigenfunctions Matrix decomposition approximation methods

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