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Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs

IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1736-1741页

作者： Vora, Manav Thangeda, Pranay Grussing, Michael N. Ornik, Melkior Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA US Army Corps Engineers Engineer Res & Dev Ctr Champaign IL 61822 USA

Partially Observable Markov Decision Processes (POMDPs) provide an efficient way to model real-world sequential decision making processes. Motivated by the problem of maintenance and inspection of a group of infrastructure components with independent dynamics, this letter presents an algorithm to find the optimal policy for a multi-component budget-constrained POMDP. We first introduce a budgeted-POMDP model (b-POMDP) which enables us to find the optimal policy for a POMDP while adhering to budget constraints. Next, we prove that the value function or maximal collected reward for a special class of b-POMDPs is a concave function of the budget for the finite horizon case. Our second contribution is an algorithm to calculate the optimal policy for a multi-component budget-constrained POMDP by finding the optimal budget split among the individual component POMDPs. The optimal budget split is posed as a welfare maximization problem and the solution is computed by exploiting the concavity of the value function. We illustrate the effectiveness of the proposed algorithm by proposing a maintenance and inspection policy for a group of real-world infrastructure components with different deterioration dynamics, inspection and maintenance costs. We show that the proposed algorithm vastly outperforms the policies currently used in practice.

关键词： Heuristic algorithms Costs Maintenance engineering Planning Inspection Markov processes Computational modeling optimization algorithms distributed control

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Energy Impact of Connecting Multiple Signalized Intersections to Energy-Efficient Driving: Simulation and Experimental Results

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1297-1302页

作者： Han, Jihun Shen, Daliang Jeong, Jongryeol Di Russo, Miriam Kim, Namdoo Grave, Julien Jean Karbowski, Dominik Rousseau, Aymeric Stutenberg, Kevin M. M. Argonne Natl Lab Transportat & Power Syst Div Lemont IL 60439 USA

Vehicle-to-infrastructure (V2I) communication connects vehicles and enables collision-free and energy-efficient driving, such as eco-approaches and departures at signalized intersections. An increased connectivity range can connect multiple signalized intersections and lead to long-term energy-efficient driving using richer information. However, no published studies to date provide insights into the energy-saving potential of increasing the connectivity range. In this letter, we present a V2I-enabled eco-driving control that can perform multiple traffic signal eco-approaches, and we systematically design a large-scale simulation study to quantify the energy impact of the increased V2I range for various scenarios. Simulation results show that the V2I-enabled eco-driving control can reduce energy use by up to 40%, on average, compared to the baseline, depending on road attributes and vehicle powertrain type. We validate these findings by evaluating the controller through a vehicle-in-the-loop testing platform.

关键词： Green products Trajectory Roads Planning Biological system modeling Computational modeling Optimal control Automotive control autonomous vehicles optimal control optimization algorithms

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Uncertainty-based dynamic economic dispatch for diverse load and wind profiles using a novel hybrid algorithm

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2023年第5期25卷 4723-4763页

作者： Basak, Sourav Dey, Bishwajit Bhattacharyya, Biplab Indian Inst Technol Dept Elect Engn Indian Sch Mines Dhanbad Bihar India Gandhi Inst Engn & Technol Dept Elect & Elect Engn Gunupur Odisha India

Optimal scheduling of the conventional generating units for five dynamic test systems is percolated in this paper. When the valve point loading effect (VPE) is present, the system's fitness function becomes non-convex and nonlinear. This paper compares and contrasts among three types of wind profile formulations, namely linear, quadratic and cubic, which are used to calculate wind power from hourly wind speed to find the profile with the greatest penetration of wind power. Thereafter, the wind profiles in turns for the five test systems are used to execute dynamic economic dispatch. The optimization tool of the study was a unique hybrid algorithm created by combining the properties of the recently developed crow search algorithm (CSA) and JAYA. Results infer that maximum level of wind penetration was attained by linear wind profile and a fuel cost reduction of 8% was realized upon incorporation of the same. Also owing to its high penetration level, the least generation cost was obtained with linear wind profile when compared to quadratic and cubic ones. Furthermore, numerical results also claims that proposed hybrid CSAJAYA approach consistently yielded better quality solutions within minimum execution time without being affected by the dimension of the problem, thereby outperforming a long list of algorithms implemented for the study.

关键词： Dynamic economic dispatch VPE or valve point effect Wind power optimization algorithms CSAJAYA

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Integration of the reptile search algorithm and the adaptive neuro-fuzzy inference system enhances standardized precipitation evapotranspiration index forecasting

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Kayhomayoon, Zahra Bahmani, Mohammad Javad Milan, Sami Ghordoyee Bazrafshan, Ommolbanin Berndtsson, Ronny Payame Noor Univ Dept Geol Tehran Iran Azad Univ Fac Civil Engn Dept Water Resources Engn Tehran Iran Univ Tehran Univ Coll Agr & Nat Resources Fac Agr Technol Water Engn Dept Tehran Iran Univ Hormozgan Fac Agr Engn & Nat Resources Dept Nat Resources Engn Bandar Abbas Iran Lund Univ Ctr Adv Middle Eastern Studies Lund Sweden Lund Univ Div Water Resources Engn Lund Sweden

A novel metaheuristic algorithm called the reptile search algorithm (RSA) was introduced in conjunction with artificial neural fuzzy inference system (ANFIS) for the estimation of standardized precipitation evapotranspiration index (SPEI). The model was tested in three different climates: arid and super-cold, semi-arid and cold, and semi-arid and moderate climate across Iran by combining meteorological indices (minimum temperature, maximum temperature, average temperature, precipitation, and potential evapotranspiration) and large-scale climate signals (North Atlantic Oscillation, Arctic Oscillation, Pacific Decadal Oscillation, and Southern Oscillation Index). The results of the ANFIS + RSA model were compared with those of the ANFIS + WOA and ANFIS + GWO models for evaluation. Based on the estimation results and error evaluation criteria, the performance of the ANFIS + RSA model is considered appropriate, showing a higher relative accuracy compared to ANFIS, ANFIS + GWO, and ANFIS + WOA. In semi-arid and moderate climates, the ANFIS + RSA model exhibited the highest prediction accuracy, with RMSE = 0.28, MAE = 0.20, CA = 0.19, and NASH = 0.91. In semi-arid and cold climates, the model's accuracy was slightly lower, with RMSE = 0.33, MAE = 0.23, CA = 0.23, and NASH = 0.85. In arid and super-cold climates, the model's accuracy remained relatively consistent, with RMSE = 0.24, MAE = 0.18, CA = 0.19, and NASH = 0.84. Furthermore, the promising results of the hybrid ANFIS + RSA model can be further evaluated in other regions and climates to assess its overall effectiveness.

关键词： Drought Extreme events Machine learning optimization algorithms Large-scale climate signals Standardized precipitation evapotranspiration index (SPEI)

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A Unified Early Termination Technique for Primal-Dual algorithms in Mixed Integer Conic Programming

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 2803-2808页

作者： Chen, Yuwen Ning, Catherine Goulart, Paul Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

We propose an early termination technique for mixed integer conic programming within branch-and bound based solvers. Our approach generalizes previous early termination results for ADMM-based solvers to a broader class of primal-dual algorithms, including both operator splitting and interior point methods. The complexity for checking early termination is O(n) for each termination check assuming a bounded problem domain. We show that this domain restriction can be relaxed for problems whose data satisfies a simple rank condition, in which case each check requires an O(n(2)) solve using a linear system that is factored only once at the root node. We further show how this approach can be used in hybrid model predictive control problems with bounded inputs. Numerical results show that our method leads to a moderate reduction in the computational time required for branch-and-bound conic solvers with interior-point based subsolvers.

关键词： optimization algorithms mixed integer programming model predictive control

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How to Escape Sharp Minima with Random Perturbations 41

How to Escape Sharp Minima with Random Perturbations

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41st International Conference on Machine Learning (ICML)

作者： Ahn, Kwangjun Jadbabaie, Ali Sra, Suvrit MIT Cambridge MA 02139 USA Microsoft Res Redmond WA 98052 USA Tech Univ Munich Munich Germany

Modern machine learning applications have witnessed the remarkable success of optimization algorithms that are designed to find flat minima. Motivated by this design choice, we undertake a formal study that (i) formulates the notion of flat minima, and (ii) studies the complexity of finding them. Specifically, we adopt the trace of the Hessian of the cost function as a measure of flatness, and use it to formally define the notion of approximate flat minima. Under this notion, we then analyze algorithms that find approximate flat minima efficiently. For general cost functions, we discuss a gradient-based algorithm that finds an approximate flat local minimum efficiently. The main component of the algorithm is to use gradients computed from randomly perturbed iterates to estimate a direction that leads to flatter minima. For the setting where the cost function is an empirical risk over training data, we present a faster algorithm that is inspired by a recently proposed practical algorithm called sharpness-aware minimization, supporting its success in practice.

关键词： optimization algorithms

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APPLICATION OF optimization ALGORITHM ON PARAMETERS OF AN EMPIRICAL VIV PREDICTION TOOL 41

APPLICATION OF OPTIMIZATION ALGORITHM ON PARAMETERS OF AN EM...

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ASME 41st International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Yin, Decao Wu, Jie Lie, Halvor Jin, Jingzhe Passano, Elizabeth Saevik, Svein Eidnes, Solve Grytoyr, Guttorm Tognarelli, Michael A. Andersen, Torgrim Igland, Ragnar Karunakaran, Daniel Gaskill, Collin SINTEF Ocean Trondheim Norway NTNU Trondheim Norway SINTEF Digital Oslo Norway Equinor Fornebu Norway BP Amer Prod Houston TX USA Kongsberg Maritime Asker Norway Aker Solut Trondheim Norway Subsea7 Stavanger Norway CRP Subsea Skelmersdale England

ISBN: (纸本)9780791885925

Conventional vortex-induced vibration (VIV) prediction tools are semi-empirical, in other words, based on several empirical parameters extracted from model tests in laboratory. Generally, the lab tests are costly, include small scale test conditions and with a limited test matrix. The extracted empirical databases are not directly applicable to full-scale VIV predictions of various slender marine structures. Therefore, large safety factors have been used by industry for VIV prediction in the past decades. To reduce the uncertainty (e.g. over-conservatism) related to semi-empirical VIV prediction tools, the NLPQL algorithm for parameter optimization of a semi-empirical time-domain prediction tool has been investigated. This methodology was demonstrated on pure cross-flow VIV prediction in an earlier study. It was shown that by setting appropriate constraints and cost functions of the optimization algorithm, this method is feasible to improve the VIV prediction accuracy. In this study, the NLPQL optimization algorithm was applied for combined cross-flow and in-line VIV predictions using time domain numerical model. Selected cases from field measurements representing multifidelity data were used to validate and verify the method.

关键词： optimization algorithms Vortex-induced vibration Cross-flow Uncertainty Marine structures Safety Computer simulation algorithms optimization Databases

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Symmetry-informed transferability of optimal parameters in the quantum approximate optimization algorithm

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Physical Review A 2025年第2期111卷 022418-022418页

作者： Isak Lyngfelt Laura García-Álvarez Department of Microtechnology and Nanoscience (MC2) Chalmers University of Technology SE-412 96 Göteborg Sweden

One of the main limitations of variational quantum algorithms is the classical optimization of the highly dimensional nonconvex variational parameter landscape. To simplify this optimization, we can reduce the search space using problem symmetries and typical optimal parameters as initial points if they concentrate. In this article, we consider typical values of optimal parameters of the quantum approximate optimization algorithm for the MAXCUT problem with d-regular tree subgraphs and reuse them in different graph instances. We prove symmetries in the optimization landscape of several kinds of weighted and unweighted graphs, which explains the existence of multiple sets of optimal parameters. However, we observe that not all optimal sets can be successfully transferred between problem instances. We find specific transferable domains in the search space and show how to translate an arbitrary set of optimal parameters into the adequate domain using the studied symmetries. Finally, we extend these results to general classical optimization problems described by Ising Hamiltonians, the Hamiltonian variational ansatz for relevant physical models, and the recursive and multiangle quantum approximate optimization algorithms.

关键词： optimization algorithms

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Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources 27

Energy-Aware AI-based Optimal Cloud Infra Allocation for Pro...

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27th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Agomuo, Okechukwu Clement Jnr, Osei Wusu Brempong Muzamal, Junaid Hussain Univ Utah Dept Operat & Informat Syst Salt Lake City UT 84132 USA Univ Utah Khalert Sch Comp Salt Lake City UT USA Fast NUCES Fast Sch Comp Lahore Pakistan

ISBN: (纸本)9798350391961;9798350391954

This paper presents a comprehensive study on optimizing resource allocation in cloud computing environments using an ensemble of machine learning techniques and optimization algorithms. We developed a multifaceted approach, integrating Long Short-Term Memory (LSTM) networks for forecasting resource demands, Particle Swarm optimization (PSO) for initial resource allocation, Q-learning for dynamic resource adjustment, and Linear Regression (LR) for predicting energy consumption. Our LSTM model demonstrated high accuracy in demand forecasting, with detailed performance metrics indicating its effectiveness in diverse scenarios. The PSO algorithm significantly enhanced the efficiency of resource distribution, evidenced by a reduction in the number of utilized units. Q-learning contributed to the system's adaptability, optimizing resource allocation based on changing demands in real-time. The LR model accurately predicted energy consumption, aligning closely with observed data and highlighting the potential for energy-efficient cloud management.

关键词： Cloud Computing Resource Allocation Machine Learning LSTM Particle Swarm optimization Q-learning Linear Regression Energy Efficiency optimization algorithms Cloud Resource Management

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SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity 41

SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Al...

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41st International Conference on Machine Learning (ICML)

作者： Chu, Tianshu Xu, Dachuan Yao, Wei Zhang, Jin Beijing Univ Technol Inst Operat Res & Informat Engn Beijing 100124 Peoples R China Southern Univ Sci & Technol Natl Ctr Appl Math Shenzhen Shenzhen Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen Peoples R China

While stochastic bilevel optimization methods have been extensively studied for addressing large-scale nested optimization problems in machine learning, it remains an open question whether the optimal complexity bounds for solving bilevel optimization are the same as those in single-level optimization. Our main result resolves this question: SPABA, an adaptation of the PAGE method for nonconvex optimization in (Li et al., 2021) to the bilevel setting, can achieve optimal sample complexity in both the finite-sum and expectation settings. We show the optimality of SPABA by proving that there is no gap in complexity analysis between stochastic bilevel and single-level optimization when implementing PAGE. Notably, as indicated by the results of (Dagreou et al., 2022), there might exist a gap in complexity analysis when implementing other stochastic gradient estimators, like SGD and SAGA. In addition to SPABA, we propose several other single-loop stochastic bilevel algorithms, that either match or improve the state-of-the-art sample complexity results, leveraging our convergence rate and complexity analysis. Numerical experiments demonstrate the superior practical performance of the proposed methods.

关键词： optimization algorithms

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