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32nd International Symposium on Graph Drawing and Network Visualization, GD 2024

作者： Baumann, Jakob Rutter, Ignaz Sudholt, Dirk University of Passau Germany

ISBN: (纸本)9783959773430

Evolutionary algorithms (EAs) are universal solvers inspired by principles of natural evolution. In many applications, EAs produce astonishingly good solutions. To complement recent theoretical advances in the analysis of EAs on graph drawing [1], we contribute a fundamental empirical study. We consider the so-called One-Sided Bipartite Crossing Minimisation (OBCM): given two layers of a bipartite graph and a fixed horizontal order of vertices on the first layer, the task is to order the vertices on the second layer to minimise the number of edge crossings. We empirically analyse the performance of simple EAs for OBCM and compare different mutation operators on the underlying permutation ordering problem: exchanging two elements (exchange), swapping adjacent elements (swap) and jumping an element to a new position (jump). EAs using jumps easily outperform all deterministic algorithms in terms of solution quality after a reasonable number of generations. We also design variations of the best-performing EAs to reduce the execution time for each generation. The improved EAs can obtain the same solution quality as before and run up to 100 times faster. © Jakob Baumann, Ignaz Rutter, and Dirk Sudholt.

关键词： consensus algorithm

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Optimal Batched Linear Bandits 41

Optimal Batched Linear Bandits

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41st International Conference on Machine Learning, ICML 2024

作者： Ren, Xuanfei Jin, Tianyuan Xu, Pan University of Science and Technology of China China National University of Singapore Singapore Duke University United States

We introduce the E4 algorithm for the batched linear bandit problem, incorporating an Explore-Estimate-Eliminate-Exploit framework. With a proper choice of exploration rate, we prove E4 achieves the finite-time minimax optimal regret with only O(log log T) batches, and the asymptotically optimal regret with only 3 batches as T → ∞, where T is the time horizon. We further prove a lower bound on the batch complexity of linear contextual bandits showing that any asymptotically optimal algorithm must require at least 3 batches in expectation as T → ∞, which indicates E4 achieves the asymptotic optimality in regret and batch complexity simultaneously. To the best of our knowledge, E4 is the first algorithm for linear bandits that simultaneously achieves the minimax and asymptotic optimality in regret with the corresponding optimal batch complexities. In addition, we show that with another choice of exploration rate E4 achieves an instance-dependent regret bound requiring at most O(log T) batches, and maintains the minimax optimality and asymptotic optimality. We conduct thorough experiments to evaluate our algorithm on randomly generated instances and the challenging End of Optimism instances (Lattimore & Szepesvari, 2017) which were shown to be hard to learn for optimism based algorithms. Empirical results show that E4 consistently outperforms baseline algorithms with respect to regret minimization, batch complexity, and computational efficiency. Copyright 2024 by the author(s)

关键词： consensus algorithm

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A Self-Tuning Version for the Fuzzy-Possibilistic Product Partition c-Means algorithm

A Self-Tuning Version for the Fuzzy-Possibilistic Product Pa...

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

作者： Naghil, Mirtill-Boglairka Kreinovich, Vladik Kovacs, Levente Szilagyi, Laszlo Doctoral School of Applied Mathematics of Óbuda University Budapest Hungary Computational Intelligence Research Group of Sapientia University Tg. Mures Romania University of Texas El PasoTX United States Óbuda University Physiological Controls Research Center Budapest Hungary Óbuda University Physiological Controls Research Center Budapest1034 Hungary

ISBN: (纸本)9781665410205

The fuzzy-possibilistic product partition c-means (FPPPCM) algorithm was proposed as a robust solution to the c-means clustering problem, in which outlier data behave similarly to distant objects in gravity systems. Although FPP-PCM reliably provides fine partitions when its parameters are well chosen, things can be difficult when it is not initialized properly. To avoid such cases, this paper proposes a self-tuning version of the FPPPCM algorithm, which incorporates some cluster size controlling variables into the objective function that allow for the adjustment of the so-called possibilistic penalty terms during the alternative optimization process. The proposed method was evaluated using four standard test datasets in three different scenarios: (1) no added noise;(2) a single outlier added;(3) multiple noisy items added. The partitions provided by the proposed algorithm were evaluated based on cluster purity, normalized mutual information and adjusted Rand index, and was compared with the outcome of previous clustering models. The proposed method performed better or at least at the same quality level at previous ones, while reducing the number of parameters the user is responsible for. © 2024 IEEE.

关键词： consensus algorithm

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Distributed Augmented Primal-Dual algorithm for Multi-Agent Constrained Optimization 43

Distributed Augmented Primal-Dual Algorithm for Multi-Agent ...

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43rd Chinese Control Conference, CCC 2024

作者： Du, Kaixin Meng, Min Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Tongji University College of Electronics and Information Engineering Department of Control Science and Engineering Shanghai201804 China

ISBN: (纸本)9789887581581

This paper deals with the distributed optimization problem while respecting coupled affine equality constraints. Resorting to the augmented Lagrangian function, a novel distributed augmented primal-dual algorithm is developed by virtue of the gradient tracking and dynamic consensus mechanisms. Based on the carefully constructed Lyapunov function, it is proved that the proposed algorithm can achieve linear convergence and an exact upper bound on the stepsize is given. Finally, a numerical example is presented to corroborate the effectiveness of the designed algorithm. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： consensus algorithm

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Research on Recognition of Precursor Anomaly Data Based on Improved Reverse Selection algorithm 6

Research on Recognition of Precursor Anomaly Data Based on I...

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6th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Qin, Le Zhang, Yuerong Chen, Lili Chen, Jun Zhang, Bo School of Information and Architecture Engineering Anhui Open University Anhui Hefei230022 China Anhui Seismological Bureau Anhui Seismological Observatory Anhui Hefei230000 China Chuzhou Seismological Bureau Chuzhou Seismological Monitoring Department Anhui Chuzhou239000 China

ISBN: (纸本)9781837242672

In order to solve the problems of low accuracy of seismic precursor anomaly data detection method and long-term reliance on manual observation and calculation to identify precursor anomalous data, this paper proposes a precursor anomaly data identification method with improved Reverse Selection algorithm, which uses the seismic precursor data provided by the China Earthquake Network as the input index, and uses the particle swarm optimization PSO optimization Reverse Selection algorithm to train the dimensionally reduced data, so as to achieve the purpose of identifying abnormal data. It solves the problem that the traditional Reverse Selection algorithm can only be set artificially and cannot independently determine the self-radius Rs, and the algorithm anomaly recognition rate is improved. Compared with other anomaly data detection algorithms such as traditional Reverse Selection algorithm and support vector machine algorithm, the running speed, global search ability, stability of operation, and prediction rate of abnormal data recognition rate of the system are improved. © The Institution of Engineering & Technology 2024.

关键词： consensus algorithm

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Distributed Difference of Convex Optimization 60

Distributed Difference of Convex Optimization

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Khatana, Vivek Salapaka, Murti V. University of Minnesota Department of Electrical and Computer Engineering Minneapolis United States

ISBN: (纸本)9798331541033

In this article, we focus on solving a class of distributed optimization problems involving n agents with the local objective function at every agent i given by the difference of two convex functions fi and gi (difference-of-convex (DC) form), where fi and gi are potentially nonsmooth. The agents communicate via a directed graph containing n nodes. We create smooth approximations of the functions fi and gi and develop a distributed algorithm utilizing the gradients of the smooth surrogates and a finite-time approximate consensus protocol. We term this algorithm as DDC-consensus. The developed DDC-consensus algorithm allows for non-symmetric directed graph topologies and can be synthesized distributively. We establish that the DDC-consensus algorithm converges to a stationary point of the nonconvex distributed optimization problem. The performance of the DDC-consensus algorithm is evaluated via a simulation study to solve a nonconvex DC-regularized distributed least squares problem. The numerical results corroborate the efficacy of the proposed algorithm. © 2024 IEEE.

关键词： consensus algorithm

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algorithmizing Aspects of Some Combinatorial Block-Designs Implemented in Network Systems 7th

Algorithmizing Aspects of Some Combinatorial Block-Designs I...

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Frolov, Alexander Kochetova, Natalya Klyagin, Anton National Research University Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9783031535512

The main numerical characteristics of a number of combinatorial block designs (CBD), which are essential for choosing parameters of network systems of combinatorial block structure are given. In addition to the traditionally considered blocks of CBD, attention is paid to dual blocks, formed by combinations of blocks containing a certain element. By combining blocks and dual blocks in one block design the so-called combined CBD is generated, the elements of which are pairs (block, element of this block). This achieves the scaling effect of the network system. The stage of its synthesis consists in the selection of CBD in accordance with the requirements of the parameters of the network system and its subsystems, as well as the links between subsystems. The main result are sets of algorithms for computing blocks, dual blocks as well as intersections of two blocks and two dual blocks for systems of Steiner triples, projective and affine geometries and transversal combinatorial block-designs. They are used to derive algorithms for computing lists of blocks, each of which intersects each of two given non-intersecting blocks, as well as algorithms for constructing block designs in general. The algorithms are given in two notations - algebraic and numeric, i.e. with representation of inputs and outputs by identifiers of elements or blocks or by numbers of these identifiers at a certain numbering by initial non-negative integers. For this purpose, the corresponding numbering algorithms and numbering inversion algorithms are used. #COMESYSO1120 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： consensus algorithm

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Distributed Robust Privacy Preservation ICC-based algorithm for Economic Dispatch in Smart Grids with Communication Delay via Using Chebyshev Polynomial 6

Distributed Robust Privacy Preservation ICC-based Algorithm ...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Wang, Aijuan Ding, Xue College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9798350353594

This paper design the incremental cost consensus-based (ICC-based) algorithm to tackle the economic dispatch problem (EDP) in smart grids (SGs) with privacy preservation guaranteed via using Chebyshev polynomial. Robustness of the proposed ICC-based EDP algorithm is reflected in that communication delay between adjacent agents is considered, and the communication topology can be time-varying with row stochastic connection matrix. Moreover, the privacy of exchanged state information is preserved by using Chebyshev polynomial instead of adding random noise method. Comparing with the common adding random noise method, this kind of privacy-preserving method can achieve the accurate convergence of the algorithm. Subsequently, the convergence of the algorithm and the optimal solution of EDP are analyzed by detailed theoretical proof. It can be found that the convergence value of the proposed algorithm is the optimal solution of EDP, and the communication delay with the uniform upper bound can be allowed. Finally, the simulation experiment is given to verify the theoretical results. © 2024 IEEE.

关键词： consensus algorithm

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Distributed cooperative cruise control for high-speed trains with multi-objective optimization

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IET CONTROL THEORY AND APPLICATIONS 2022年第16期16卷 1645-1656页

作者： Huang, Zhiwu Wang, Pingping Zhou, Feng Chen, Bin Wu, Yue Liu, Yongjie Li, Fei Wang, Jing Peng, Jun Cent South Univ Sch Automat Changsha Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha Peoples R China Bradley Univ Sch Elect & Comp Engn Peoria IL USA Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

This paper proposes a novel cooperative cruise control strategy to jointly optimize the energy consumption and ride comfort for multiple high-speed trains. The cooperative control strategy is constructed by combining the consensus algorithm and potential function to achieve the synchronization of train speed and keep a safe tracking interval. A multi-objective optimal function of multiple high-speed trains is constituted of cruise following error, energy consumption and ride comfort. The non-dominated quick sorting algorithm (NSGA-II) is used to find the global Pareto-optimal solutions to obtain the optimal cooperative control gains, which achieve the optimal trade-offs between energy consumption and riding comfort for high-speed trains. The asymptotic stability of the proposed control strategy is proved strictly. The simulation results verify that the proposed control strategy can effectively reduce energy consumption and improve the riding comfort simultaneously when compared with existing methods.

关键词： multiobjective optimization consensus algorithm multiobjective optimal function riding comfort cruise control strategy cruise following error energy consumption global Pareto-optimal solutions potential function optimal control asymptotic stability optimal trade-offs distributed cooperative cruise control optimal cooperative control gains high-speed trains nondominated quick sorting algorithm railways velocity control Pareto optimisation sorting

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Distributed control strategy of DC microgrid based on consistency theory

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ENERGY REPORTS 2022年 8卷 739-750页

作者： Zhang, Nannan Yang, Dongsheng Zhang, Huaguang Luo, Yanhong Northeastern Univ Dept Elect Engn Shenyang 110819 Peoples R China

In order to solve the cooperative control problem among multiple distributed units in a distributed DC microgrid, a distributed control based on a consensus algorithm is firstly proposed, which can realize power distribution among distributed units. The distributed voltage control of the observer will generate steady-state errors due to problems such as time delay initial value of the observer. A voltage optimization control strategy based on the PI consistency algorithm is further proposed, which converts the voltage control problem into an optimization problem, and the optimization goal is to ensure the deviation sum of each bulbar from the rated value is the smallest, and the stability of this method is proved. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink software. (C) 2022 Published by Elsevier Ltd.

关键词： DC microgrid Distributed optimal control consensus algorithm Multi-agent system

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