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Safe Pricing Mechanisms for Distributed Resource Allocation With Bandit Feedback

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第4期11卷 2010-2021页

作者： Hutchinson, Spencer Turan, Berkay Alizadeh, Mahnoosh Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability. Existing approaches to pricing design for safety-critical networks often require that users are queried beforehand to negotiate prices, which has proven to be challenging to implement in the real world. To offer a more practical alternative, we develop learning-based pricing mechanisms that require no input from the users. These pricing mechanisms aim to maximize the utility of the users' consumption by gradually estimating the users' price response over a span of $T$ time steps (e.g., days) while ensuring that the infrastructure network's safety constraints that limit the users' demand are satisfied at all time steps. We propose two different algorithms for the two different scenarios when: the utility function is chosen by the central coordinator to achieve a social objective, and the utility function is defined by the price response under the assumption that the users are self-interested agents. We prove that both algorithms enjoy $\tilde{\mathcal {O}} (T<^>{2/3})$ regret with high probability. We then apply these algorithms to demand response pricing for the smart grid and numerically demonstrate their effectiveness.

关键词： Safety Resource management Pricing Vectors optimization Demand response Network systems Demand side management multi-armed bandit problem smart grid

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multispecies design decision-making: A hybrid computational model for ecological building envelope optimization and evaluation

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JOURNAL OF BUILDING ENGINEERING 2025年 108卷

作者： Selvan, Surayyn Uthaya Windorfer, Laura Saroglou, Soultana Tanya Calbi, Mariasole Culshaw, Victoria Roccotiello, Enrica Grobman, Jacob Yasha Barath, Shany Technion Israel Inst Technol Fac Architecture & Town Planning IL-3200003 Haifa Israel Tech Univ Munich Sch Life Sci Dept Life Sci Syst Terr Ecol Res Grp Hans Carl von Carlowitz Pl 2 D-85350 Freising Weihenstephen Germany Univ Genoa Dept Earth Environm & Life Sci Lab Plant Biol Environm Bot Viale Benedetto XV 5 I-16132 Genoa Italy

Urbanization causes habitat fragmentation and biodiversity loss, factors often overlooked in traditional nature-based solutions that prioritize human-centric benefits. In response, ecologically inclusive design approaches, such as multispecies building envelopes, have emerged to support cohabitation between human and other-than-human stakeholders. To facilitate these interactions, this paper presents a computational hybrid multi-criteria decision-making model that optimizes and evaluates ecological building envelopes. Integrating multi-objective and multi-attribute decision-making techniques into parametric workflows, the model optimizes schematic envelope alternatives using ecologically informed performance indicators. A case study applied to a generic building typology in a Mediterranean climate demonstrates the model's ability to negotiate contextual objectives such as visual comfort, material use, energy performance, and ecological suitability. Tested under varied multispecies stakeholder priority scenarios and compared to a benchmark non-vegetated envelope, the optimized alternatives complied with local window-to-wall ratio regulations while distributing up to 1339.8 m(3) of soil coverage. These configurations reduced summer solar radiation by more than 70 % through self-shading and enhanced multispecies interactions by increasing proxies for plant species presence and climbing animal connectivity on the envelope. Model accuracy was validated using cross-platform comparison (MAE <2.7E-07;RMSE <3.1E-07), and sensitivity analysis confirmed ranking robustness. While limitations include ecological simplifications and durability assumptions, the model offers a practical and adaptable interdisciplinary tool for integrating ecological knowledge into early-stage design decision-making for holistically sustainable solutions.

关键词： Abiotic

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BIM-based multi-objective optimization of clash resolution: A NSGA-II approach

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JOURNAL OF BUILDING ENGINEERING 2024年 89卷

作者： Liu, Xinnan Zhao, Junxiang Yu, Yi Ji, Yingbo North China Univ Technol Sch Civil Engn Beijing 100144 Peoples R China China Commun Informat Technol Grp Co Ltd Beijing 100088 Peoples R China

With the wide application of building information modelling (BIM) in the Architectural, Engineering, and Construction (AEC) industry, holistic approaches based on the network theory to improve clash detection and resolution processes have received increasing attention. The previous studies using holistic approaches in the clash resolution mainly focus on single objective optimization. However, in engineering practice of BIM-based clash resolution, finding optimal solutions for a single-objective that meet the project's requirements is challenging. Moreover, optimization objectives need to be appropriately balanced because these objectives tend to conflict with each other, which leads to a typical multi-objective optimization problem. In this paper, by taking minimum number and minimum moving distance of components to be changed as optimization objectives, a second-generation non-dominated sorting genetic algorithm (NSGAII) approach to solve the multi-objective optimization problem is proposed based on component spatial relation network. Clash solutions are automatically generated under the spatial movement constraints and system priority constraints for clash components. The proposed method is validated by clash resolution of an underground parking lot in a residential project, and the results show that clash solutions generated by the proposed method is practical and acceptable. The proposed method is promising in facilitating the practical application of BIM-based automatic clash resolution.

关键词： BIM Component spatial relation network Clash resolution multi-objective optimization NSGA-II

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Vertical-Axis Wind Turbine Design Using Surrogate-assisted optimization with Physical Experiments In-loop 23

Vertical-Axis Wind Turbine Design Using Surrogate-assisted O...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lette, Matthew Rahi, Kamrul Hasan Singh, Hemant Kumar Ray, Tapabrata Univ New South Wales Canberra ACT Australia Australian Dept Def Sydney NSW Australia

ISBN: (纸本)9798400701191

Most of the existing wind power comes from the use of traditional Horizontal-Axis Wind Turbines (HAWT), which typically require installation over vast countryside areas (in wind farms) due to considerations such as wake effects and noise. Recently, there has been an increasing interest to explore an alternative solution - VerticalAxis Wind Turbines (VAWT) - that may be more suitable for compact urban areas. In this paper, we conduct design optimization of twin-blade VAWTs by evaluating the candidate designs through direct small-scale prototyping and physical experiments in-loop. The problem is a practical example of expensive optimization where the number of evaluations affordable are severely limited. In addition to the conventional single-objective form (maximize rotational speed), we also solve a multi-objective version of the problem (maximize rotational speed and minimize mass). For conducting optimization, we leverage surrogate-assisted approaches that make use of both predicted mean and uncertainties in modeling for an efficient exploration of the design space. The experiments demonstrate that the approach is able to generate non-intuitive designs that are competitive or better than the baseline (classic twin-blade Savonius design) within a small evaluation budget. The study also strengthens the case for applying this approach for design optimization in general.

关键词： Vertical-axis wind turbine multi-objective optimization Experiment in-loop Expensive optimization

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Robust Congestion Control for Demand-Based optimization in Precoded multi-Beam High Throughput Satellite Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第10期70卷 6918-6937页

作者： Van-Phuc Bui Trinh Van Chien Lagunas, Eva Grotz, Joel Chatzinotas, Symeon Ottersten, Bjorn Aalborg Univ Dept Elect Syst DK-9000 Aalborg Denmark Hanoi Univ Sci & Technol HUST Sch Informat & Commun Technol SoICT Hanoi 100000 Vietnam Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg Luxembourg SES L-6815 Betzdorf Luxembourg

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users' quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.

关键词： optimization Quality of service Throughput Satellites Resource management Precoding Neural networks multi-beam high throughput satellite communications quality of service requirements multi-objective optimization neural networks

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A survey on evolutionary computation for complex continuous optimization

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 59-110页

作者： Zhan, Zhi-Hui Shi, Lin Tan, Kay Chen Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Hanyang Univ Ansan South Korea Chaoyang Univ Technol Taichung Taiwan

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

关键词： Evolutionary computation (EC) Evolutionary algorithm (EA) Swarm intelligence (SI) Complex continuous optimization problems Large-scale optimization Dynamic optimization multi-modal optimization Many-objective optimization Constrained optimization Expensive optimization Function-oriented taxonomy

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A NEW METHOD OF PARAMETER IDENTIFICATION FOR PROTON EXCHANGE MEMBRANE FUEL CELL BASED ON HYBRID PARTICLE SWARM optimization WITH DIFFERENTIAL EVOLUTION ALGORITHM

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THERMAL SCIENCE 2023年第5B期27卷 4209-4222页

作者： Liu, Dong Yang, Xiangguo Guan, Cong Qi, Tianxi Zheng, Qinggen Wuhan Univ Technol Key Lab High Performance Ship Technol Minist Educ Wuhan Peoples R China

With the characteristics of high energy conversion efficiency, high energy density and low operating temperature, the proton exchange membrane fuel cells (PEMFC) have become one of the green energy sources with broad prospects. The establishment of accurate mathematical model of the PEMFC is the basis of simulation and control strategy. At present, some intelligent algorithms have certain drawbacks, and can hardly find the balanced point between precision and computational time. In this study, a novel parameter identification approach combining the hybrid particle swarm optimization (PSO) algorithm with differential evolution, i.e. hybrid DEPSO, is proposed to obtain the unknown parameters in the PEMFC mathematical model and solve the problems of premature convergence of PSO and poor global search ability of differential evolution. Six benchmark functions are applied to verify the performance of the algorithm. The results prove that the hybrid DEPSO can evade local optimum preferably while having swifter convergence rate. Two PEMFC stacks are investigated and modeled. In order to evaluate the accuracy of model, the sum of squared errors between the measured voltage and the estimated output voltage are examined. Numerical results show higher accuracy of the hybrid DEPSO-based model comparing with other recently published optimization approaches. Furthermore, the simulation results indicate that the accuracy of the PEMFC model optimized by the hybrid DEPSO algorithm improves 0.19-1.86%, which can provide a new solution the multi-objective optimization problem and promote the practical application of the PEMFC.

关键词： PEMFC parameters identification PSO algorithm differential evolution algorithm

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An Improved Surrogate-Assisted multi-objective Evolutionary Algorithm Based on Heterogeneous Ensemble

An Improved Surrogate-Assisted Multi-Objective Evolutionary ...

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第43届中国控制会议

作者： Li Wenlu Guo Nan Qiao Junfei Peng Yixin Liu Jiahui Sun Yueyang Jia Yuxin Information Science Department Beijing University of Technology College of Carbon Neutrality Future Technology Beijing University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the practical industrial field,optimizing parameter configurations during factory operations is an indispensable step,especially for multi-objective optimization problems(MOPs) that involve high computational,economic costs or extensive numerical simulations,which was categorized as Expensive multi-objective optimization Problems(EMOPs).To efficiently and reasonably evaluate fitness in the face of EMOP,it is essential to develop a surrogate model to minimize costly expensive function evaluations and assist the entire optimization *** study proposes a Surrogate-Assisted Evolutionary Algorithm SFEA that integrates Support Vector Machines(SVM) and Feedforward Neural Networks(FNN).SFEA constructs surrogate models using heterogeneous integration methods to mitigate the limitations of a single-model approaches in complex and variable optimization scenarios,thereby enhancing the efficiency and quality of the optimization ***,this paper proposes an adaptive sampling strategy and a corresponding sample filling mechanism within a dual-archive management framework,based on the convergence and diversity indices of the *** validation on multi-objective DTLZ and WFG benchmark problems confirms the effectiveness and feasibility of SFEA in addressing expensive multi-objective optimization challenges.

关键词： Support Vector Machine(SVM) feedforward neural network(FNN) model management surrogate-assisted evolutionary algorithm(SAEA) multi-objective optimization algorithm(MOPs)

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A multi-objective honey badger approach for energy efficiency enhancement of the hybrid pressure retarded osmosis and photovoltaic thermal system

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JOURNAL OF ENERGY STORAGE 2023年 59卷

作者： Chen, Yingxue Feng, Guanxiang Chen, Huatao Gou, Linfeng He, Wei Meng, Xianlong Northwestern Polytech Univ Sch Automation Xian Peoples R China Univ Warwick Sch Engn Coventry England

The pressure retarded osmosis process is emerging as one of the most promising technologies for extracting renewable energy from salinity gradient resources. The hybrid system of the pressure retarded osmosis and photovoltaic/thermal systems is capable of effectively improving overall energy efficiency. The prime research focus of hybrid energy systems is to produce environment-friendly solutions by minimizing energy consumption and improving energy efficiency. Due to the increasing demand for intelligent computing, the challenges of maximum power extraction and efficient energy management have attracted the attention of researchers around the world. Thus, a Honey Badger Approach based energy management strategy is proposed to solve the problem of performance enhancement in the pressure retarded osmosis and photovoltaic/thermal system by maximizing the multi-variable functions. The Honey badger approach uses global sight to tackle the optimization of multiple objectives and to achieve optimal solutions. The proposed energy management method is tested with various types of operational environments, including variations in the operational temperature, solar irradiance and ambient temperature in regard to detrimental effects. The practical hourly data of Perth for a year is utilized to simulate the actual operating conditions. The simulation results indicate that the proposed energy management method overwhelms other competing approaches with better energy efficiency, thereby ensuring the continuity of energy supply and improving the reliability of the system.

关键词： Renewable energy optimization Hybrid system Honey badger approach Energy management

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multi-objective Design optimization of an IPMSM Drive System Based on Loss Minimization Control Strategy

Multi-objective Design Optimization of an IPMSM Drive System...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Liu, Lin Guo, Youguang Lei, Gang Yin, Wenliang Zhu, Jianguo University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9798350338362

To enhance the optimization and control efficiencies of the interior permanent magnet synchronous motor (IPMSM) drive system for electric vehicles (EVs), an improved multi-objective design optimization strategy at the system level is proposed in this paper. To handle the high dimensional and nonlinear problems for searching Pareto optimal solutions, the multi-level optimization algorithm with approximate models is utilized, by which the computational cost can be greatly reduced. In terms of the typical specifications and requirements of EVs, the maximum torque, minimum torque ripple, and minimum speed overshoot are set as the optimization objectives. For further improving the optimization accuracy and search speed, the objective of minimum loss is achieved through the loss minimization control algorithm instead of being solved in each optimization level, by which the system efficiency is also promoted. Experimental and numerical results show that the proposed approaches can effectively solve the design optimization issues of electric drive systems, and enhance the static and dynamic system performance. © 2023 IEEE.

关键词： multiobjective optimization

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