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检索条件"任意字段=4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022"

共 109 条记录，以下是1-10 订阅

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2022 4th international conference on data-driven optimization of complex systems, docs 2022

2022 4th International Conference on Data-Driven Optimizatio...

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4th international conference on data-driven optimization of complex systems, docs 2022

ISBN: (纸本)9781665459822

the proceedings contain 82 papers. the topics discussed include: data-driven robust distributed MPC for collision avoidance formation navigation of constrained nonholonomic multi-robot systems;cooperative adaptive identification and force control for networked Euler-Lagrange systems;IDPSO for influence maximization under independent cascade model;research on calibration method of extrinsic parameters of lidar and camera carried by UAV;robust dynamic positioning of marine surface vessels under denial of service attacks;research on destination images with information containing sentiment classification driven by multi-source data;reinforcement learning for robust neuro-control of constrained nonlinear systems;unbalanced data processing for software defect prediction;adaptive heading tracking control of intelligent ship with stochastic noise and dead zone output;evolution of online public opinions and situational control during the COVID-19 pandemic: a case study from Chengdu, China;looking into the word-of-mouth in tourist accommodations based on big data;fault diagnosis of indicator diagram of pumping well based on stochastic configuration network;continuous berth allocation considering carbon emission and uncertainty;and topology design for multiple unmanned surface vessels cooperative control in polymorphic networks.

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Urban Cold Chain Logistics Vehicle Path optimization Based on IOT Real-Time Traffic 4

Urban Cold Chain Logistics Vehicle Path Optimization Based o...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Huang, Yanan Pan, Lin Wuhan University of Technology School of Transportation and Logistics Engineering Wuhan430063 China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

As the standard of living continues to improve,people' s daily requirement for fresh products is also growing. this study considers the real-time traffic congestion, divides the road sections, and calculates the actual travel time of each sub-section, analyzes the transportation cost, establishs a mathematical model with the goal of minimizing the cost and solved by improved genetic algorithm. In addition, this study explores effects of discriminate traffic congestion levels on vehicle paths and costs. © 2022 IEEE.

关键词： Traffic congestion

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Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method 4

Support vector machine based on Genetic Algorithm integrated...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Li, Huaijian Fang, Jing Du, Xiaojing Hu, Ziye Beijing Institute of Technology School of Aerospace Engineering Beijing China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In order to solve the problem of low fault detection rate of combinatorial navigation due to the mismatch of support vector machine parameters, this paper uses genetic algorithm and lattice search method to find the optimal support vector machine penalty parameter C and kernel function parameter g. the result of the search is brought into the support vector machine to obtain the classification model and finally classify the combinatorial navigation data. the results show that the genetic algorithm has a faster search speed and a higher classification accuracy. © 2022 IEEE.

关键词： Genetic algorithms

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Robot Manipulator Anti-Disturbance Control based on PSO Multi-task optimization 4

Robot Manipulator Anti-Disturbance Control based on PSO Mult...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Chen, Yifan Qu, Miaomiao Shi, Xuhua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

A new optimal anti-disturbance sliding mode control approach for manipulators is proposed in this paper. Aiming at the difficulty of parameter selection of sliding mode controller for manipulators, instead of empirical trial and error design approach, it is proposed a multi-task transfer strategy of surrogate-assisted Particle Swarm optimization (PSO) approach, to solve the problem of optimal control parameter selection in the time-consuming adjustment process. the experimental results show that compared with the traditional PSO algorithm, the approach in this paper can effectively improve the convergence speed and control effect. the performance of the controller based on this optimization approach is superior to that based on the traditional PSO algorithm in terms of dynamic and static performance. © 2022 IEEE.

关键词： Sliding mode control

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data-driven robust distributed MPC for collision avoidance formation navigation of constrained nonholonomic multi-robot systems 4

Data-driven robust distributed MPC for collision avoidance f...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Fu, Junjie Wen, Guanghui Southeast University School of Mathematics Nanjing211189 China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In this work, we consider the robust collision avoidance formation navigation problem for multiple constrained nonholonomic robots with uncertain dynamics. Distributed model predictive control (MPC) based method is proposed in view of its ability to handle the input and state constraints of the robots explicitly. A synchronous non-iterative distributed algorithm is employed which reduces the communication requirement of the system. Furthermore, to enable the state trajectory prediction under uncertain robot dynamics, a data-driven online learning method is proposed to generate an accurate model of the nonholonomic robots adaptively. Based on the proposed control strategy, it is shown that robust collision avoidance formation navigation is successfully achieved while the input and state constraints of the robots are satisfied. Simulation examples are given to demonstrate the performance of the data-driven learning method and the distributed MPC based formation navigation controller. © 2022 IEEE.

关键词： Collision avoidance

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Semi-Supervised Prototype Network with CBAM and data Selector for Few-Shot Bearing Fault Diagnosis 4

Semi-Supervised Prototype Network with CBAM and Data Selecto...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Zhou, Wenkang Li, Ning Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In this paper, an improved semi-supervised prototypical network method is proposed to improve the performance of the bearing fault diagnosis model in the context of data scarcity. Firstly, a metric-based meta-learning method, the prototype network model, is introduced to train a general fault feature learner. And an attention mechanism module is introduced to the model to better extract the fault features of bearings. Secondly, unlabeled fault data are utilized to tune the original prototype in a semi-supervised manner to improve the performance of the model. thirdly, to reduce the disturbing influence of new classes of data in unlabeled samples on prototypes, a data selector is designed in the semi-supervised model. the proposed method is verified on the public bearing fault dataset, and outperforms the common machine learning methods and meta learning methods. © 2022 IEEE.

关键词： Failure analysis

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A Reference Point and Multi-direction Search Based Evolution Algorithm for Large-scale Multi-objective optimization 4

A Reference Point and Multi-direction Search Based Evolution...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Tian, Shuai Wang, Ziqing Wu, Xiangjuan Wang, Yuping Xidian University School of Computer Science and Technology Xi'an China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

this paper proposes a new algorithm based on a reference point selection mechanism and a multi-direction search strategy for large-scale multi-objective optimization problems. Firstly, a center point symmetry strategy is designed to select uniformly distributed reference points and transform the original problem into several low-dimensional single-objective optimization problems. Based on the reference points, a multi-directional weight variable association strategy is proposed to add search directions for the original problem and to improve the search ability of the algorithm. then, to solve the transformed single-objective problem effectively, an improved differential evolution algorithm based on center mutation is presented. Finally, the numerical experiments are conducted on the large-scale optimization problem benchmarks LSMOP with 200, 500, and 1000 decision variables and the comparison of the proposed algorithm with four state-of-the-art algorithms is made. the results show that the proposed algorithm significantly outperforms the compared algorithms. © 2022 IEEE.

关键词： Multiobjective optimization

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data-driven Optimal Control for Multi-Player Non-Zero-Sum Games with Unknown Dynamics 4

Data-Driven Optimal Control for Multi-Player Non-Zero-Sum Ga...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Zhu, Liao Xia, Hongbing Hou, Jiaxu Guo, Ping Beijing Normal University at Zhuhai International Academic Center of Complex Systems Zhuhai China Beijing Normal University School of Systems Science Beijing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

this paper focuses on optimal control problems of discrete-time nonlinear multi-player non-zero-sum games with unknown dynamics. Based on adaptive dynamic programming, a data-driven adaptive critic control method is developed to obtain the optimal strategies. In order to solve multi-player non-zero-sum games, a new globalized dual heuristic dynamic programming design is proposed without a model network. the coupled Hamilton-Jacobi equations are solved by previous and current value functions for the temporal difference errors. Neural networks are used to approximate value functions and optimal strategies, respectively. the weight updating rules for critic networks and action networks are tuned based on the observing data accrued along system trajectories. the stability analysis of all neural network weights is given by the Lyapunov approach. Simulation results are included to verify the performance of the proposed optimal control scheme. © 2022 IEEE.

关键词： Dynamic programming

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Clustering-based Autoencoding for Dynamic Multiobjective Evolutionary optimization 4

Clustering-based Autoencoding for Dynamic Multiobjective Evo...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Ye, Yulong Zhu, Qingling Li, Lingjie Chen, Jianyong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Dynamic multiobjective optimization problems (DMOPs) usually contain multiple conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, transfer learning (TL)-based methods have been considered promising in solving DMOPs. However, most existing TL-based methods are computationally extensive and therefore time-consuming. In this paper, a clustering based autoencoding for DMOEA, called CAE-DMOEA, is proposed, which aims to generate a high-quality initial population to accelerate the evolutionary process and improve the optimization performance. In particular, by learning the mapping relationship between the regional centroids of the approximate Pareto-optimal solutions (POS) from the previous two environments, the CAE-DMOEA can effectively predict the regional centroids of the POS in the new environment, which helps to tracking the moving POS and enhance the optimization efficiency. To study the performance of the proposed method, extensive experiments have been carried out by comparing three state-of-the-art DMOEAs. the experimental results show that the overall performance of the CAE-DMOEA is superior to that of the compared algorithms. © 2022 IEEE.

关键词： Evolutionary algorithms

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Proximal Policy optimization for Explainable Recommended systems 4

Proximal Policy Optimization for Explainable Recommended Sys...

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4th international conference on data-driven optimization of complex systems, docs 2022

作者： Feng, Qian Xiao, Geyang Liang, Yuan Zhang, Huifeng Yan, Linlin Yi, Xiaoyu Beijing Normal University School of Statistics Beijing China Intelligent Network Research Institute Zhejiang Lab Hangzhou China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In this paper, in order to intuitively improve the interpretability of the recommendation, we make full use of the knowledge graph and provide an 'explicit' recommendation path for the recommended result. Specifically, we embeds the entities and relations in the knowledge graph according to consumers' behavior. then a knowledge reasoning method based on reinforcement learning algorithm is proposed, which aims to find a reasonable path after two entities in the knowledge graph are given. the main contributions of this paper are as follows: the current State-of-the-art method in reinforcement learning, PPO is used for improvement. the soft-reward function after adjustment behaves better for the current problem while training. And a Q-value based path exploration method is innovated for testing. With extensive experiments on several real-world datasets provided by Amazon, our method behaves better for the current problem compared with the baseline. © 2022 IEEE.

关键词： Reinforcement learning

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