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25th Conference on Economics and Computation, EC 2024

作者： Rubinstein, Aviad Zhao, Junyao Stanford University United States

ISBN: (纸本)9798400707049

We study repeated first-price auctions and general repeated Bayesian games between two players, where one player, the learner, employs a no-regret learning algorithm, and the other player, the optimizer, knowing the learner’s algorithm, strategizes to maximize its own utility. For a commonly used class of no-regret learning algorithms called mean-based algorithms, we show that (i) in standard (i.e., full-information) first-price auctions, the optimizer cannot get more than the Stackelberg utility - a standard benchmark in the literature, but (ii) in Bayesian first-price auctions, there are instances where the optimizer can achieve much higher than the Stackelberg utility. On the other hand, Mansour et al. (2022) showed that a more sophisticated class of algorithms called no-polytope-swap-regret algorithms are sufficient to cap the optimizer’s utility at the Stackelberg utility in any repeated Bayesian game (including Bayesian first-price auctions), and they pose the open question whether no-polytope-swap-regret algorithms are necessary to cap the optimizer’s utility. For general Bayesian games, under a reasonable and necessary condition, we prove that no-polytope-swap-regret algorithms are indeed necessary to cap the optimizer’s utility and thus answer their open question. For Bayesian first-price auctions, we give a simple improvement of the standard algorithm for minimizing the polytope swap regret by exploiting the structure of Bayesian first-price auctions. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Many-Objective Evolutionary Algorithm Based on Spectral Clustering and Population Distribution

Many-Objective Evolutionary Algorithm Based on Spectral Clus...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Yao, Jiaqi Liu, Bin School of Computer and Mathematical Sciences the University of Adelaide 5005 Australia Haide College Ocean University of China Qingdao266071 China School of Mathematical Sciences Ocean University of China Qingdao266071 China

ISBN: (纸本)9798331517021

Numerous real-world applications involve complex many-objective optimization problems characterized by a large number of objective functions. Despite the development of various algorithms to address this kind of problems, these approaches often suffer from insufficient selection pressure, complex Pareto front shapes, and uneven distribution of solutions. To address this issue, this paper introduces a many-objective evolutionary algorithm based on spectral clustering and population distribution. We first utilize spectral clustering to classify the objective functions based on their correlations and aggregate them within the same class to reducing the complexity of the problem. Then, we propose a knowledge-guided decomposition based evolutionary algorithm to solve the simplified model. Finally, we conduct a series of comparative experiments to evaluate the effectiveness of the proposed method. The experimental results indicate that our method demonstrates excellent performance across various problems. © 2024 IEEE.

关键词： optimization algorithms

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0 Factor Analysis 63

0 Factor Analysis

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Linyang Liu, Wanquan Zhu, Bin Sun Yat-sen University School of Intelligent Systems Engineering Gongchang Road 66 Shenzhen518107 China

ISBN: (纸本)9798350316339

Factor Analysis is about finding a low-rank plus sparse additive decomposition from a noisy estimate of the signal covariance matrix. In order to get such a decomposition, we formulate an optimization problem using the nuclear norm for the low-rank component, the 0 norm for the sparse component, and the Kullback-Leibler divergence to control the residual in the sample covariance matrix. An alternating minimization algorithm is designed for the solution of the optimization problem. The effectiveness of the algorithm is verified via simulations on synthetic and real datasets. © 2024 IEEE.

关键词： optimization algorithms

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Distribution System Planning through Integrated Distributed Generation using the Shuffled Frog Leaping Algorithm 4

Distribution System Planning through Integrated Distributed ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Mohan, S. Nayanatara, C. Shweta, Kundena Swetha, B.J. Sri Sairam Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331508845

This research presents a new & integrated method to tackle the difficulties in distribution planning (DSP). It does this including distributed generation(DG), which promise in areas controlled by distribution utilities. The framework blends advanced optimization techniques with expert knowledge to find the best sizing placement of DG resources. The main goal is to both investment & operational linked to DG deployment. This includes covering system losses while also considering different scenarios. The model is designed to meet rising load demands. Interestingly, the optimized model uses the Shuffled Frog Leaping Algorithm, which helps achieve accurate & reliable planning results. Through a thorough present worth analysis of various scenarios, this study examines how viable it is to integrate DG as a key strategy for solving DSP issues. Overall, this integrated model contributes significantly to improving distribution system planning and offers a strategic way that merges distributed generation with strict optimization techniques, such as using the Shuffled Frog Leaping Algorithm effectively. © 2024 IEEE.

关键词： optimization algorithms

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An Intelligent Evaluation Method for Medium Voltage Distribution Line Loss

An Intelligent Evaluation Method for Medium Voltage Distribu...

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2024 Boao New Power System International Forum - Power System and New Energy Technology Innovation Forum, NPSIF 2024

作者： Chen, Zhanqi Wu, Zhongming Wang, Zhijian Zhao, Ruxuan China Southern Power Grid Guangdong Co. Ltd Zhaoqing Sihui Power Supply Bureau Zhaoqing China

ISBN: (纸本)9798331504557

Given the limitations of traditional line loss assessment methods in terms of learning efficiency and prediction accuracy, this study chose the 10kV medium voltage distribution network as the research objective. After conducting an in-depth analysis of various factors affecting line loss, we refined the BP neural network algorithm using the particle swarm optimization algorithm, thereby proposing a new intelligent line loss evaluation strategy. This strategy initially enhances evaluation quality by reducing the dimensionality of line loss data, and subsequently employs the particle swarm optimization algorithm to determine the optimal threshold and weight, ultimately improving the training efficiency of BP. The research results indicate that, compared to the combination of the particle swarm optimization algorithm, backpropagation algorithm, genetic algorithm, and particle swarm optimization algorithm, the accuracy of the intelligent line loss evaluation method has been improved by 14.2%, 19.1 %, and 6.17% respectively, and it demonstrates good performance in prediction accuracy and convergence. © 2024 IEEE.

关键词： optimization algorithms

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A Framework for Decentralized Equilibrium Seeking in Networked Sampled-Data Games using Hybrid Control Tools 22

A Framework for Decentralized Equilibrium Seeking in Network...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Shenoy, Vishal Poveda, Jorge I. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781713872344

We study the steady-state Nash equilibrium-seeking problem for sampled-data games with LTI dynamics and quadratic costs. The key challenge is to guarantee the robust stability and convergence properties of the closed-loop system in the presence of local individual sampling mechanisms assigned to each of the players in the game. This problem is non-trivial due to the unstable behaviors that can arise when sequential control updates (rather than parallel) emerge in the closed-loop system because of the existence of local control triggering mechanisms in each node of the network. To address this issue, we introduce a controls framework based on tools from hybrid dynamical systems theory. Our results are illustrated via numerical examples. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Equilibrium-Seeking Network Systems Hybrid Control optimization algorithms Cyber-Physical Systems

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Micro-IDE: A tool platform for generating efficient deployment alternatives based on microservices

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SOFTWARE-PRACTICE & EXPERIENCE 2022年第7期52卷 1756-1782页

作者： Aksakalli, Isil Karabey Celik, Turgay Can, Ahmet Burak Tekinerdogan, Bedir Erzurum Tech Univ Dept Comp Engn Erzurum Turkey BITES Def & Aerosp Ankara Turkey Hacettepe Univ Dept Comp Engn Ankara Turkey Wageningen Univ & Res Informat Technol Grp Wageningen Netherlands

Microservice architecture (MSA) is a paradigm to design and develop scalable distributed applications using loosely coupled, highly cohesive components that can be deployed independently. The applications that realize the MSA may contain thousands of services that together form the overall system. Microservices interact with each other by producing and consuming data. Deploying frequently communicating services to the same physical resource would reduce network utilization, which is vital for reducing costs and improving scalability. Since the physical resources have limited capacity, it is not always possible to deploy communicating services to the same resource. Therefore, automated efficient deployment alternatives need to be generated for MSA in the design phase. To address this problem, we proposed an algorithmic approach to generate efficient microservice deployment configurations to available cloud resources in our previous study. In this study, a tool (Micro-IDE) has been proposed to realize and evaluate this approach. The Micro-IDE tool has been validated using a case study inspired by the Spotify application.

关键词： automated deployment of microservices cloud computing microservice architectures optimization algorithms tool platform for deploying microservices

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Resource scheduling in cloud-based manufacturing system: a comprehensive survey

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期122卷 4201-4219页

作者： Rashidifar, Rasoul Bouzary, Hamed Chen, F. Frank Univ Texas San Antonio Dept Mech Engn One UTSA Circle San Antonio TX 78249 USA Univ Texas San Antonio Ctr Adv Mfg & Lean Syst One UTSA Circle San Antonio TX 78249 USA

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated.

关键词： Cloud manufacturing system Resource scheduling optimization algorithms Machine learning algorithms Mathematical model

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Distributed Economic Dispatch for Multi-area Power Systems Based on Multi-objective Differential Evolution Algorithm 13

Distributed Economic Dispatch for Multi-area Power Systems B...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Ren, Xuyang Bu, Xuhui Yin, Yanling Li, Xuan Zhang, Yan School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo454003 China School of Business Administration Henan Polytechnic University Jiaozuo454003 China College of Business Zhengzhou Technology and Business University Zhengzhou451400 China

ISBN: (纸本)9798350361674

Economic dispatch of a multi-area interconnected power system with wind, solar, and energy storage units is a typical non-convex and nonlinear optimization problem, which is difficult to be solved by the existing mathematical programming methods. In this paper, a distributed multi-objective differential evolution algorithm is proposed. Firstly, to improve the global search ability of the multi-objective differential evolutionary algorithm, the population is partitioned using a competitive mechanism. A multi-strategy scheme is used according to the partition to enhance the diversity of the algorithm. Secondly, the IEEE 39-bus system is decomposed into several sub-areas using the bus tearing method and the interval information is transmitted through the boundary tie-lines. Then a distributed method is proposed to be applied to the above algorithm, where each sub-algorithm solves the sub-problem associated with each area. Finally, the IEEE 39-bus system is divided into two-area and three-area interconnected systems, and the performance of the algorithm is verified on the modified IEEE 39-bus system. The experimental results verify the effectiveness of the improved multi-objective differential evolution optimization algorithm and the feasibility of the distributed approach. © 2024 IEEE.

关键词： optimization algorithms

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A Novel Reinforcement Learning Multi-Objective Community Detection Algorithm with E-Gradient-Greedy Strategy

A Novel Reinforcement Learning Multi-Objective Community Det...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wei, Wenhong Meng, Yi Li, Qingxia School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China School of Artificial Intelligence Dongguan City University Dongguan523419 China

ISBN: (纸本)9781665410205

Accurately categorizing communities within a social network is a crucial aspect of community detection, carrying significant practical relevance. To achieve a higher quality of community division, we combine reinforcement learning methods to learn the distribution characteristics of community nodes during the iteration process of the algorithm, which guides the nodes to migrate to other communities and enhances the algorithm's global search capability. Through a new E -gradient-greedy strategy, which can obtain a node's gradient information relative to its neighbors and achieve higher performance in local search. To speed up the adaptability of the algorithm at the beginning of the iteration and to alleviate the resolution limitation imposed by modularity optimization, this paper employs a triangular subnetwork-based weight assignment method for balancing the weights of each edge in the network. Experimental results on real-world and synthetic network datasets demonstrate that our method's community identification precision outperforms recent community detection algorithms, exhibiting higher accuracy, higher resolution, and adaptability to various network characteristics and structural changes. © 2024 IEEE.

关键词： optimization algorithms

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