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Joint optimal sizing and operation scheduling of a power-to-gas hub based on a linear program

APPLIED ENERGY 2025年 379卷

作者： Rukavina, Filip Sundrica, Marijo Karneluti, Antonio Vasak, Mario Univ Zagreb Fac Elect Engn & Comp Lab Renewable Energy Syst Unska 3 HR-10000 Zagreb Croatia

With rising challenges of greening the gas sector and decreasing wasteful curtailment of renewable energy sources, power-to-gas hubs seem to be a suitable solution for mitigation of both. Power-to-gas hubs can act as energy storage and as sector coupling solution between electricity, gas, heat, and water grids, but also agricultural markets when using biomass and/or biochar. However, the economic viability and adequate operation of such hubs is crucial in order to make a difference. The optimization procedure developed tackles the complex problem of finding the optimal structure of a power-to-gas hub and simultaneously optimizing its operation. The unique aspects of the proposed procedure include: i) finding the optimal system structure across a large number of possible technologies, processes and storage components, rather than optimizing a power-togas hub with a single mass/energy conversion pathway;ii) collocation of a power-to-gas hub with an existing renewable energy plant and/or an existing industrial plant;and iii) viable computational complexity while short-term intermittence of renewable sources is included. After comprehensive techno-economic modelling of the overall setup, the developed optimization procedure is used on three case studies showcasing the strengths of the procedure. The case studies show how the optimal system configuration and optimal components' sizes change depending on prices of electricity and natural gas, and on possibility to sell digestate and/or biochar. Also, the case studies show the influence of electricity sources, or rather the intermittence of renewables, on optimal operating schedules.

关键词： Optimization Sizing Scheduling linear programming

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A robust optimization approach to budget optimization in online marketing campaigns

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2025年 1-32页

作者： Nehezova, Tereza Sedlarova Kvasnicka, Roman Brozova, Helena Hlavaty, Robert Stanislavska, Lucie Kvasnickova Czech Univ Life Sci Prague Fac Econ & Management Dept Syst Engn Prague Czech Republic Czech Univ Life Sci Prague Fac Econ & Management Dept Management & Mkt Prague Czech Republic

This paper presents a novel approach to strategic budget allocation in online marketing campaigns using robust optimization techniques. We develop a linear programming model that specifically addresses uncertainties in conversion cost coefficients, a critical challenge in digital marketing performance measurement. The model's distinctive feature lies in its combination of robust optimization with fuzzy linguistic scales, which enables marketing professionals to express uncertainty levels in their expert estimations qualitatively. Using real campaign data obtained from advertising platform management tools, we demonstrate how the proposed robust counterpart transforms traditional deterministic optimization into a more resilient decision-making framework. Our comparative analysis between deterministic and robust approaches reveals that while the robust solution may sacrifice nominal optimality, it provides protection against cost coefficient fluctuations typically encountered in online marketing. The key contribution is a practical decision support tool that allows marketers to systematically incorporate their risk preferences and uncertainty expectations into budget allocation decisions. Case study demonstrates the model's ability to optimize performance while managing risks in digital advertising markets with volatile conversion costs.

关键词： linear programming Online marketing Robust optimization Uncertainty

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Robust Neural Differential Solution for Time-Dependent Nonlinear Optimization With Noises Rejection

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 418-432页

作者： Wei, Lin Jin, Long Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Qinghai Normal Univ State Key Lab Tibetan Intelligent Informat Proc & Xining 810000 Peoples R China

Model uncertainty, exogenous disturbance, and time-dependent computing are significant issues in constrained nonlinear optimization and related practical problems. This paper highlights two limitations of current methods for time-dependent nonlinear optimization, i.e., disturbance handling and restrictions on the objective function and constraints. First, a novel robust neural differential (RND) solution is proposed for solving equality- and inequality-constrained time-dependent nonlinear optimization (EIC-TDNO) problems involving the suppression of nonspecific disturbances. Second, the dynamic vector-valued error equation equivalent to the EIC-TDNO method is derived. Theoretical proofs ensure that the proposed RND solution converges to zero at an exponential rate, eliminates polynomial noise, and effectively reduces the time-varying disturbance. Finally, through simulation and applications to 2-D filter design and robot motion planning, the RND solution is verified to exhibit rapid convergence, high precision, strong robustness, and versatility in comparison with the existing models.

关键词： Optimization Mathematical models linear programming Time-varying systems Task analysis Robot motion Stability analysis Time-dependent nonlinear optimization robust neural differential solution time-varying noises robot motion planning

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Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 418-432页

作者： Zhang, Yunchuan Park, Sangwoo Simeone, Osvaldo Kings Coll London Ctr Intelligent Informat Proc Syst CIIPS Dept Engn Kings Commun Learning & Informat Proc KCLIP Lab London WC2R 2LS England

In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. Furthermore, higher-fidelity evaluations of the optimization objectives often entail a larger cost. Existing multi-fidelity black-box optimization strategies select candidate solutions and fidelity levels with the goal of maximizing the information about the optimal value or the optimal solution for the current task. Assuming that successive optimization tasks are related, this paper introduces a novel information-theoretic acquisition function that balances the need to acquire information about the current task with the goal of collecting information transferable to future tasks. The proposed method transfers across tasks distributions over parameters of a Gaussian process surrogate model by implementing particle-based variational Bayesian updates. Theoretical insights based on the analysis of the expected regret substantiate the benefits of acquiring transferable knowledge across tasks. Furthermore, experimental results across synthetic and real-world examples reveal that the proposed acquisition strategy that caters to future tasks can significantly improve the optimization efficiency as soon as a sufficient number of tasks is processed.

关键词： Optimization Costs Closed box linear programming Bayes methods Entropy Resource management Multitasking Information theory Vectors Bayesian optimization multi-fidelity simulation entropy search knowledge transfer

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Sparse Multi-View K-Means Clustering

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IEEE ACCESS 2025年 13卷 46773-46793页

作者： Yang, Miin-Shen Parveen, Shazia Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters. The k-means algorithm assigns equal importance to all features in a single view. Several multi-view clustering techniques have been developed over the last few decades, including multi-view k-means (MVKM). In MVKM, patterns can be detected more accurately, and clustering performance is improved by applying each view individually for pattern discovery. The demand for highly accurate and flexible methods in machine learning and artificial intelligence has increased the use of multi-view datasets. These datasets are complicated and pose challenges for researchers in real-life situations. A novel type of sparse k-means clustering algorithm, sparse MVKM (S-MVKM), is proposed in this study for multi-view data. The main aim of our proposed S-MVKM algorithm is to achieve sparsity in features and shrink unimportant features to exactly zero using Lasso (least absolute shrinkage and selection operator). For considering theoretic behaviors of the S-MVKM algorithm, we create a convergence theorem to ensure that any convergent subsequence in S-MVKM will ultimately tend to an optimal solution. The proposed S-MVKM algorithm is then compared with several existing algorithms, such as traditional MVKM, feature reduction MVKM (FR-MVKM), MVKM with adaptive sparse membership weight allocation (M-VASM), and redundant and sparse feature learning (RSF-MVKM). Based on the three clustering performance measures of accuracy rate, Rand index, and normalized mutual information, we make the comparisons between them by using several synthetic and real-world datasets. The comparative results demonstrate that the proposed S-MVKM clustering algorithm effectively surpasses these competing clustering algorithms in terms of these three performance measures. Finally, we make concluding remarks that provide potential g

关键词： Clustering algorithms linear programming Machine learning algorithms Convergence Accuracy Data mining Social networking (online) Partitioning algorithms Internet of Things Industries K-means multi-view k-means (MVKM) sparsity sparse MVKM (S-MVKM) feature reduction performance measures

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Distributed Neurodynamic Models for Solving a Class of System of Nonlinear Equations

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 486-497页

作者： Han, Xin He, Xing Ju, Xingxing Che, Hangjun Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Sichuan Univ Arts & Sci Coll Math Dazhou 635000 Sichuan Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China Texas A&M Univ Qatar Dept Math Doha Qatar

This article investigates a class of systems of nonlinear equations (SNEs). Three distributed neurodynamic models (DNMs), namely a two-layer model (DNM-I) and two single-layer models (DNM-II and DNM-III), are proposed to search for such a system's exact solution or a solution in the sense of leastsquares. Combining a dynamic positive definite matrix with the primal-dual method, DNM-I is designed and it is proved to be globally convergent. To obtain a concise model, based on the dynamic positive definite matrix, time-varying gain, and activation function, DNM-II is developed and it enjoys global convergence. To inherit DNM-II's concise structure and improved convergence, DNM-III is proposed with the aid of time-varying gain and activation function, and this model possesses global fixed-time consensus and convergence. For the smooth case, DNM-III's globally exponential convergence is demonstrated under the Polyak-Lojasiewicz (PL) condition. Moreover, for the nonsmooth case, DNM-III's globally finite-time convergence is proved under the Kurdyka-Lojasiewicz (KL) condition. Finally, the proposed DNMs are applied to tackle quadratic programming (QP), and some numerical examples are provided to illustrate the effectiveness and advantages of the proposed models.

关键词： Mathematical models Convergence Neurodynamics Minimization Numerical models linear programming Nonlinear equations Distributed neurodynamic models (DNMs) fixed-time consensus and finite-time convergence Kurdyka-ojasiewicz (KL) and Polyak-ojasiewicz (PL) conditions nonlinear equations

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Hybrid subQUBO Annealing With a Correction Process for Multi-Day Intermodal Trip Planning

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IEEE ACCESS 2025年 13卷 19716-19727页

作者： Noguchi, Tatsuya Fukada, Keisuke Bao, Siya Togawa, Nozomu Waseda Univ Dept Comp Sci & Commun Engn Shinjuku Tokyo 1698555 Japan

The multi-day intermodal trip planning problem (MITPP) is an optimization problem that seeks to create the optimal route to visit Point-of-Interest (POI) and hotels over days. This problem involves coordinating intermodal transportation, such as walking, public transportation, to create a well-crafted travel itinerary. Quantum annealers have recently been explored as a powerful tool for solving combinatorial optimization problems by converting the problems into Quadratic Unconstrained Binary Optimization (QUBO). However, current quantum annealers have a small QUBO input size so that they cannot directly solve large-scale MITPPs. In this paper, we address this issue by extracting a subQUBO from the original large QUBO based on variable (spin) deviations and randomness. Then, we iteratively solve the subQUBOs by the quantum annealer and update the (quasi-)optimal solution. As the obtained (quasi-)optimal solution may violate constraints, we apply the correction processing till all constraints are satisfied. According to the experiment results using a real quantum annealer, our proposed method obtained high-quality solutions for large-scale MITPPs in the Tokyo area, and compared to the full QUBO method, we achieve a maximum spin reduction of 98.9%. Especially, compared to the method by a conventional computer and two conventional subQUBO methods, POI satisfaction is improved by 10.2%, and travel costs are improved by 23.2% respectively.

关键词： Annealing Quantum annealing Optimization Costs Planning Solid modeling Computational modeling Public transportation Magnetic fields linear programming Trip planning problem intermodal quantum annealer Ising model subQUBO correction process

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Steady and Transient State Performance Balancing Strategy of Adaptive Complex-Vector Filter in PMSM Sensorless Control

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Du, Pengcheng Wang, Bo Xu, Dianguo Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China

In sensorless control of permanent magnet synchronous motors (PMSMs), the complex-vector filter (CVF) attached behind the observer is effective at suppressing harmonics. However, a fixed gain parameter cannot match the requirements of different working conditions, i.e., it cannot balance harmonic elimination and dynamic performance. To deal with this issue, a gain adaptive law of CVF is studied based on the reconstructed objective function. First, the frequency-locked loop (FLL) is embedded in the CVF to increase its stability margin. Then, the reason why the CVF output signal cannot serve as the objective function is analyzed. Thus, an objective function is developed that can correlate the speed error with the CVF gain. Based on this objective function, the gain-adaptive law of CVF is obtained with the gradient ascent method. For the studied adaptive iterative algorithm, its influence on the stability and dynamics of the position estimator system is investigated. The effectiveness of the studied adaptive law is tested by the experiments on a 2.3-kW PMSM platform.

关键词： Phase locked loops Frequency locked loops Harmonic analysis Power harmonic filters linear programming Employee welfare Stators Observers Bandwidth Adaptive systems Adaptive gain parameter adjustment complex-vector filter (CVF) permanent magnet synchronous motor (PMSM) position-sensorless control

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UBETI: A DV-Hop Localization Algorithm for IoT With Distance Upper Bound Estimation by Triangle Inequality

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IEEE INTERNET OF THINGS JOURNAL 2025年第3期12卷 2445-2457页

作者： Wang, Qiran Shen, Zhengdi West New York New Jersey NJ USA Pittsburgh Pennsylvania PA USA

Internet of Things (IoT) localization is crucial in smart applications. Distance vector hop (DV-Hop)-based algorithms offer cost-effective and scalable solutions relying on connectivity information. However, most algorithms have limitations in irregular areas, including a tendency to overestimate the distances between nodes. To address this issue, we propose distance upper bound estimations by triangle inequality (UBETI) to adjust the DV-Hop objectives and mitigate the distance overestimation in irregular areas. In addition, we propose a crossover strategy for the genetic algorithms (GAs) in the optimization, named stats-crossover (ST-CR). ST-CR generates the offspring of the population leveraging the statistical data of the parent population and improves the optimization performance. The two proposed strategies can be incorporated into existing DV-Hop algorithms and boost their performance. Evaluation results show that compared with other highly regarded methods, the proposed algorithm has a meaningful improvement, particularly in irregular areas. Ablation studies demonstrate the effectiveness of each step in the proposed algorithm.

关键词： Estimation Location awareness Optimization linear programming Internet of Things Accuracy Upper bound Crossover distance estimation model distance vector hop (DV-Hop) genetic algorithm (GA) Internet of Things (IoT) localization multiobjective optimization

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Multi-Population Kidney-Inspired Algorithm With Migration Policy Selections for Feature Selection Problems

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IEEE ACCESS 2025年 13卷 6306-6320页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Leng Goh, Say Zakree Ahmad Nazri, Mohd Othman, Zalinda Kamrul Hasan, Mohammad Alvankarian, Fatemeh Iranian eUnivers Fac Comp Engn Tehran *** Iran Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Univ Malaysia Sabah Kampus Antarabangsa Labuan Fac Comp & Informat Optimizat & Visual Analyt Res Grp Labuan 87000 Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur Bangi 43600 Selangor Malaysia Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

Optimization algorithms often encounter challenges in effectively managing the trade-off between exploration and exploitation, usually leading to less-than-optimal outcomes. This study introduces two novel migration policies in multi-population version of kidney-inspired algorithm (KA) to address this dilemma. The initial algorithm, coded as MultiPop-KA, implements a predetermined migration policy. Conversely, the second algorithm, coded as AutoMultiPop-KA, adopts an adaptive migration policy selection process that determines migration type based on the average fitness of sub-populations. By capitalizing on a multi-population framework and incorporating two migration policies, these methods aim to achieve a more refined equilibrium between exploration and exploitation, thereby augmenting the effectiveness of the KA. Experimental evaluations, conducted across 25 test functions and applied to 18 benchmark feature selection problems, demonstrate the efficacy of the proposed techniques. These results indicate that the proposed approach can significantly enhance optimization algorithms' performance and overall quality.

关键词： Optimization Feature extraction Rough sets Filtration Convergence Blood Kidney linear programming Information science Filtering algorithms Exploration and exploitation kidney-inspired algorithm multi-population migration policy feature selection

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