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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Wang, Qianhui Ye, Yulong Zhu, Qingling Liu, Songbai Lin, Qiuzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350393521

Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set (POS).A new dynamic constrained multi-objective evolutionary algorithm with adaptive two-stage archiving and autoencoder prediction is proposed in this paper, called ATAP to effectively solve DCMOPs. Specifically, ATAP designs a differential denoising autoencoder (DDA) prediction strategy, which applies a denoising autoencoder to thoroughly analyze change trends of historical population and predict some initial solutions in the new environment. Subsequently, to promote greater diversity within the initial population, a differential rule is implemented, effectively addressing the potential scarcity of diversity caused by constraints. Moreover, ATAP introduces an adaptive two-stage archiving (ATA) constraint handling technique, which can dynamically adjust evolutionary stages based on the state of the population. this approach can adaptively determine preferences between objectives and constraints, achieving a better balance. In this way, ATA and DDA are well cooperated to efficiently solve DCMOPs with time-varying constraints and objectives. the experimental results demonstrate that the proposed ATAP is effective and has some advantages over three competitive algorithms when solving the CEC2023 DCMOP benchmark. © 2023 IEEE.

关键词： Forecasting

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A data-driven Online ADP of Exponential Convergence Based on k-nearest-neighbor Averager, Stable Term and Persistence Excitation 4

A Data-driven Online ADP of Exponential Convergence Based on...

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4th international conference on systems and Informatics (ICSAI)

作者： Huang, Zhijian Wang, Shengtang Zheng, Huan Zhang, Cheng Zhang, Guichen Wu, Qili Tan, Qinmin Yang, Zhiyuan Shanghai Maritime Univ Lab Intelligent Control & Computat Shanghai Peoples R China

ISBN: (纸本)9781538611074

With the development of marine science, aeronautics and astronautics, energy, chemical industry, biomedicine and management science, many complex systems face the problem of optimization and control. Approximate dynamic programming solves the curse of dimensionality problem of dynamic programming, and it is a new kind of approximate optimization solution that emerges in recent years. Based on the analysis of optimization system, this paper proposes a nonlinear multi-input multi-output, online learning, and data-driven approximate dynamic programming structure and its learning algorithm. the method is achieved from the following three aspects: 1) the critic function of multi-dimensional input critic module of the approximate dynamic programming is approximated with a data-driven k-nearest neighbor method;2) the multi-output policy iteration of the approximate dynamic programming actor module is calculated with an exponential convergence performance;3) the critic and actor modules are learned synchronously, and achieve the online optimal and control effect. the optimal control for the longitudinal motion of a thermal underwater glider is used to show the effect of the proposed method. this work can lay a foundation for the theory and application of a nonlinear data-driven multi-input multi-output approximate dynamic programming method. It's also the consensus needs in optimization control and artificial intelligence of many scientific and engineering fields, such as energy conservation, emission reduction, decision support and operational management etc.

关键词： approximate dynamic programming exponential convergence k-nearest-neighbor persistence excitation

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An Unconstrained Auxiliary Framework for Constrained Many-Objective optimization 5

An Unconstrained Auxiliary Framework for Constrained Many-Ob...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Qiao, Kangjia Liang, Jing Bi, Ying Yu, Kunjie Yue, Caitong Qu, Boyang School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Electrical and Information Zhongyuan University of Technology Zhengzhou China

ISBN: (纸本)9798350393521

Constrained many-objective optimization problems (CMaOPs) include the optimization of many objective functions and satisfaction of constraints, which seriously enhance the difficulty of problems. Although several constrained many-objective evolutionary algorithms (CMaOEAs) have been designed, they still have difficulties in tackling many objectives and constraints at the same time. To better solve CMaOPs, this paper proposes an unconstrained auxiliary framework, in which an auxiliary task without constraints is developed to reduce the search difficulties of constraints. Moreover, to tackle many objectives, the existing CMaOEAs are employed to address the auxiliary task, in which the constraint values of solutions are set to zeros. In the experiments, one classic CMaOEA and two latest CMaOEAs are integrated into the framework to form three new algorithms. the results show the effectiveness and superiority of the framework. Besides, the winner among three new algorithms is compared with several existing CMaOEAs and shows better results. Meanwhile, we discuss the reasons that why the unconstrained framework is effect for the existing benchmark functions. Accordingly, we refer to that new test functions are urgently needed for the development of constrained many-objective optimization. © 2023 IEEE.

关键词： Evolutionary algorithms

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Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3-D Deployment optimization of Directional Sensor Networks 5

Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Zhang, Yuntian Chen, Chen Wu, Tongyu Miao, Changhao Ding, Shuxin School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Natl. Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Traffic Management Laboratory for High-Speed Railway Beijing100081 China

ISBN: (纸本)9798350393521

A major concern in designing sensor networks is the deployment problem. However, establishing an efficient algorithm for the real-world deployment problem is challenging due to three issues, which are 1) the realistic mixed-integer nonlinear programming problem (MINLP) with mixed-variable;2) the combinatorial subset selection problem;and 3) the expensive computational cost for fitness evaluation in the 3-D coverage problem. therefore, this paper addresses these challenges and proposes a surrogate-assisted hybrid metaheuristic for mixed-variable 3-D deployment optimization of directional sensor networks (DSNs). First, an MINLP with flexible coordinate transformation technique and an efficient mixed-variable encoding scheme are introduced to model and represent the problem. We propose hybrid metaheuristic which applies two reproduction methods respectively for discrete and continuous variables. Second, we design sparse population-based incremental learning (s-PBIL) to handle inherent subset selection problem. s-PBIL could accurately learn the required information, and automatically learn a sparse distribution. third, a mixed-variable surrogate with unifying space under Bayesian model management is incorporated to reduce the expensive computational cost. Experiment results on real-world deployment scenarios scaling from small-size to large-size show the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Sensor networks

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Comparative Analysis of Surrogate Models for High-Dimensional Expensive Problems 5

Comparative Analysis of Surrogate Models for High-Dimensiona...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Huang, Xun Li, Fei Liu, Yuan-Chao School of Electrical and Information Engineering Anhui University of Technology Maanshan243032 China School of Electrical and Information Engineering Anhui University of Technology Anhui Province Engineering Laboratory of Intelligent Demolition Equipment Maanshan243032 China School of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9798350393521

To resolve the challenges posed by evaluating approximated fitness values in high-dimensional expensive optimization problems. this work primary focus on exploring the theoretical and structural aspects of four distinct surrogate models. the models under comparison are radial basis function, Broad learning system, random vector functional link neural networks, and extreme learning machines. the primary focus of the investigation encompassed the comprehensive examination of six distinct test functions, applied to both 100-D and 50-D benchmark problems. throughout the comparison, meticulous attention was directed towards two pivotal facets: accuracy and computational cost. On the other hand, the combination of the approximated fitness values and the approximated uncertainty is regarded as two distinct *** study's results revealed notable differences in performance among the examined models. Radial basis function and random vector functional link neural networks exhibited superior accuracy and scalability compared to the Broad learning system and extreme learning machines. Among these, the random vector functional link neural networks show-case the best performance across all six test functions in the 50-D benchmark problems. the implications of these findings are valuable for selecting appropriate fitness estimation models in the context of High-dimensional Expensive optimization problems. © 2023 IEEE.

关键词： optimization

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Coordinated optimization for Zone-Based Voltage Control in Distribution Grids

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 173-184页

作者： Di Fazio, Anna Rita Risi, Chiara Russo, Mario De Santis, Michele Univ Cassino Dept Elect & Informat Engn I-03043 Cassino Italy Southern Lazio I-03043 Cassino Italy Niccolo Cusano Univ Dept Engn I-00166 Rome Italy

this article addresses the problem of optimizing voltage profiles in distribution networks. the voltage optimization is split into two stages;the former is performed offline and the latter online. First, the network is partitioned into several weakly coupled voltage control zones (VCZs) with pilot nodes (PNs). then, the partitioning is used to optimize the voltage profiles of the distribution systems on the frame of a two time-scale-based coordinated approach. At the first level, a centralized voltage optimization problem (VOP), minimizing the distance of bus voltages at the PNs from their reference values and subject to linearized power flow equations, is solved to fix the positions of the on-load tap changer and of step-voltage regulators, and the reactive powers provided by capacitor banks. At the second level, the VOP is implemented according to a decentralized approach, in which the solution is obtained by applying a distributed algorithm based on the alternating direction method of multipliers. It optimizes in each VCZ the voltage at the PN by acting on the active and reactive powers provided by the distributed energy resources present in the VCZ;the VCZ solutions are driven to the global optimum of the whole distribution system by a limited data exchange between the PNs. the proposed approach reduces the complexity and computational burden typical when solving the VOP on a large scale system. the proposed strategy is tested on the modified IEEE 123-bus system;various load and generation scenarios are analyzed proving the effectiveness of the proposed approach in achieving the objective of voltage regulation.

关键词： Voltage control Reactive power optimization Voltage measurement Real-time systems Distribution networks Sensitivity Alternating direction method of multipliers (ADMM) decentralized approach distribution system distributed energy resources (DERs) voltage optimization

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EV Charging Behavior Simulation and Analysis Using Real-World Charging Load data 4

EV Charging Behavior Simulation and Analysis Using Real-Worl...

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4th international conference on Smart Power and Internet Energy systems (SPIES)

作者： Ge, Gangheng Tang, Jinrui Liu, Jianchao Yang, Honghui Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665489577

Accurate short-term load forecasting (STLF) results for charging station charging load are essential to improve the reliability and safety of power distribution systems. In modern electric vehicle (EV) optimization dispatching, in addition to accurate STLF results, it is necessary to know the status of each user's EV, and user charging behavior promptly. In this paper, an EV charging STLF method is proposed and discussed. We first derive accurate STLF results based on a data-driven method. Using the driving distance probability function of vehicle and Monte Carlo method to generate a certain number of different states of EV. Using the two-stage filling method, the number of the EVs charging at each time point in the first step is set as the model's parameters, and then the model is enabled with multiple times to retain the results which are close to results obtained by the data-driven method. the number of the EVs charging at some time point in the second step are set as the model's parameters. Based on the first step, the load is filled, and the accurate model-driven STLF results and charging behavior information are finally obtained. thus, this information is used to regulate the charging behavior of EVs.

关键词： Electric vehicle charging load load forecasting Monte Carlo model-driven travel behavior

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Envisioning Model-Based Performance Engineering Frameworks

Envisioning Model-Based Performance Engineering Frameworks

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12th international conference on Ambient systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Arcelli, Davide Univ Aquila Dept Informat Engn Comp Sci & Math Via Vetoio 1 I-67100 Laquila Italy

Our daily activities depend on complex software systems that must guarantee certain performance. Several approaches have been devised in the last decade to validate software systems against performance requirements. However, software designers still encounter problems in the interpretation of performance analysis results (e.g., mean values, probability distribution functions) and in the definition of design alternatives (e.g., to split a software component in two and redeploy one of them) aimed at fulfilling performance requirements. this paper describes a general model-based performance engineering framework to support designers in dealing with such problems aimed at enhancing the system. the framework relies on a formalization of the knowledge needed in order to characterize performance flaws and provide alternative system design. Such knowledge can be instantiated based on the techniques devised for interpreting performance analysis results and providing feedback to designers. three techniques are considered in this paper for instantiating the framework and the main challenges to face during such process are pointed out and discussed. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Software Performance Engineering Model-driven Engineering Software systems Performance Analysis Performance optimization Refactoring

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Introduction to the DDDAS2022 conference Infosymbiotics/Dynamic data driven Applications systems 4th

Introduction to the DDDAS2022 Conference Infosymbiotics/Dyna...

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4th international conference on Dynamic data driven Applications systems, DDDAS 2022

作者： Blasch, Erik Darema, Frederica MOVEJ Analytics FairbornOH United States InfoSymbiotic Systems Society BethesdaMD United States

ISBN: (纸本)9783031526695

the 4th international DDDAS 2022 conference, convened on October 6–10, featured presentations on Dynamic data driven Applications systems (DDDAS)-based approaches and capabilities, in a wide set of areas, with an overarching theme of "InfoSymbiotics/DDDAS for human, environmental and engineering sustainment". the topics included aerospace mechanics and space systems, networked communications and autonomy, biomedical and environmental systems, and featured recent techniques in generative Artificial Intelligence, theoretical Machine Learning, and dynamic Digital Twins. Capturing the tenets of the DDDAS paradigm across these areas, solutions were presented to address challenges in systems-of-systems’ approaches providing analysis, assessments and enhanced capabilities in the presence of complex and big data. the conference comprised of the main track that featured 31 plenary presentations of peer reviewed papers, five keynotes, an invited talk, and a panel on wildfires monitoring. In conjunction with the main track of the DDDAS conference, a Workshop on Climate and Life, Earth, Planets (CLEPs) was conducted, which featured 20 presentations on environmental challenges and a panel on Seismic and Nuclear Explosion monitoring. In addition to the papers of the plenary presentations in the main track of the conference, the DDDAS2022 Proceedings feature an overview of the conference, a synopsis of the main-track papers, and summaries of the keynotes and the wildfires panels followed by corresponding contributed papers by the the speakers in these sessions. Additional information and archival materials, including the presentations’ slides and recordings, are available in the DDDAS website: ***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fires

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Parallel fuzzy cellular automata for data-driven simulation of wildfire spreading

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年 21卷 469-485页

作者： Ntinas, Vasileios G. Moutafis, Byron E. Trunfio, Giuseppe A. Sirakoulis, Georgios Ch. Democritus Univ Thrace Sch Engn Dept Elect & Comp Engn Univ Campus GR-67100 Xanthi Greece Univ Sassari Dept Architecture Planning & Design Alghero Italy

Cellular Automata (CA) have been introduced many decades ago as one of the most efficient parallel computational models able to simulate various physical processes and systems where the interactions are local. In this paper, we are trying to advance the application of CA in modeling wildfires by accounting for the fuzziness intrinsic to the numerous environmental variables and mechanisms engaged with the emergence of the phenomenon itself. the proposed Fuzzy CA (FCA) model adopts a data-driven approach, based on evolutionary optimization, which allows incorporating knowledge from real wildfires in order to enhance its accuracy. the main difficulty for doing so arrives from the computational complexity of the proposed framework and the burden of computational resources needed for its application, which would prevent the real-time prediction of fire spread scenarios. In order to tackle the aforementioned difficulties, we propose model's fully parallel implementations in Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) hardware. In the article, we first investigate the speedup achieved by the developed parallel implementations. then, we present and discuss two applications to heterogeneous landscapes through comparisons with observed wildfires. Moreover, we compare the proposed framework with two different modelling approaches and results found are really promising. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Forest fire spreading Cellular automata Fuzzy theory GPU Hardware Parallelization

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