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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21-30 订阅

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Looking into the Word-of-Mouth in Tourist Accommodations Based on Big data 4

Looking into the Word-of-Mouth in Tourist Accommodations Bas...

引用

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

作者： Chen, Pingping Su, Zerui Luo, Yuyan Zhao, Ting Chengdu University of Technology School of Management Science Chengdu China Sichuan University Business School Chengdu China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Online word-of-mouth information can reflect tourists' consumption choices and behavioral patterns. Existing studies mainly analyze word-of-mouth in tourism from internal factors (service level, price, facility condition, etc.), and there is a lack of extended studies focusing on external factors, e. g. the spatial distribution of tourist accommodations. Based on the Point of Interest and User Generated Content information of 71 accommodations around Chengdu Research Base of Giant Panda Breeding, this paper uses social network and sentiment analysis tools to filter relevant variables affecting word-of-mouth. Spatial econometric models are then built and examined, and we further identified the optimal model to discuss the key factors affecting word-of-mouth in the accommodation industry from the perspective of spatial distribution. Results show that: (1) there is significant spatial clustering and spillover effect of word-of-mouth in the study accommodations. (2) network structure has a significant impact on word-of-mouth, which enables reference for site selection. (3) the improvement of service level and geographical location of tourist accommodation has a significant positive effect on word-of-mouth. © 2022 IEEE.

关键词： Sentiment analysis

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Study on the Performance optimization and Application of Big Model in Big data Processing 6

Study on the Performance Optimization and Application of Big...

引用

6th international conference on data-driven optimization of complex systems (docs)

作者： Wen, Zebin Wang, Ping Zhang, Jiuyang Xiong, Ping Guangdong Univ Sci & Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9798350377859;9798350377842

With the surge in data volume, big data processing faces unprecedented challenges, among which large models have become a hot research topic due to their powerful data processing capabilities. this paper delves into the performance bottlenecks of large models in big data processing and proposes a series of performance optimization strategies. through a review of existing data processing technologies and large model architectures, combined with optimization theory and practice, this study introduces a comprehensive optimization mechanism that includes resource scheduling, model computational efficiency, and storage and IO. Experiments were conducted in a cloud computing environment to validate these strategies. the results indicate that the optimization strategies significantly enhanced performance when processing different scales of data, improved load balancing and resource utilization, and increased system stability. this research enriches the theoretical study of big data processing and provides effective optimization avenues for the practical application of large models in fields such as data mining and parallel computing. It offers guidance for feature engineering and data preprocessing and paves the way for future research directions.

关键词： Big data Processing data Mining Parallel Computing Feature Engineering data Preprocessing

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Efficient Named Entity Recognition Based on Broad Learning System and BERT 4

Efficient Named Entity Recognition Based on Broad Learning S...

引用

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

作者： Wang, Yudi Zuo, Yi Li, Tieshan Philip Chen, C.L. Dalian Maritime University Navigation College Dalian China Dalian Maritime University Collaborative Innovation Center of Maritime Big Data & Artificial General Intelligence Navigation College Dalian China University of Electronic Science and Technology of China School of Automatic Engineering Chengdu China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In this paper, we propose an efficient approach for named entity recognition (NER). this paper applies the broad learning system (BLS) to NER task, which includes the feature learning and incremental learning processes. In the feature learning of BLS, the calculation of each new feature node involves only matrix multiplication, and can effectively reduce the amount of calculation to improve efficiency in obtaining rich features. In the incremental learning of BLS, the feature nodes are calculated by active function and ridge regression in order to obtain incremental nodes. In this paper, we combine the BLS with bidirectional encoder representations from transformers (BERT) and conditional random fields (CRF) algorithms to conduct NER of English corpus. Firstly, we extract features through BERT. Secondly, we use BLS to calculate the extracted features. Finally, we apply CRF to decoding features. In numerical experiments, NER of English corpus based on BIO schema is used as benchmark, And the proposed method shows higher accuracy and faster training than other baseline methods. © 2022 IEEE.

关键词： Learning systems

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A Path Planning Approach for Optic Inspection Based on Composite Frame 6

A Path Planning Approach for Optic Inspection Based on Compo...

引用

6th international conference on data-driven optimization of complex systems (docs)

作者： Zhang, Yukang Zheng, Jianchao Luo, Junwei Yan, Xiaohui Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Path planning for Automatic Optical Inspection (AOI) is a key strategy for enhancing the efficiency of AOI devices. this paper modeled the inspection issue upon the completion of imaging window planning and introduced a composite frame to optimize the problem. Swarm intelligence algorithms were employed to determine the position of imaging points, while the 2-opt algorithm was utilized to optimize the access order. the edge location method was proposed to reduce the solution space. To address the slow convergence and low accuracy issues for BSO algorithm, an improved version was proposed. the strategy of individual discussion was improved, the individual step length was adjusted dynamically according to the population position, and the competitive crossover strategy was introduced. Experimental results confirm the effectiveness of the improved brain storm algorithm and the composite frame.

关键词： Computation intelligence Brain storm optimization Algorithm design Path planning Automatic optical inspection

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Innovization-based Evolutionary Large-Scale Multiobjective optimization 6

Innovization-based Evolutionary Large-Scale Multiobjective O...

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6th international conference on data-driven optimization of complex systems (docs)

作者： Liu, Songbai Yan, Qianqiang Wang, Zeyi Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350377859;9798350377842

How to handle the high dimensional search space effectively is a burning research question in evolutionary computation when solving large-scale multiobjective optimization problems. To alleviate it, we propose a new evolutionary algorithm with a tactic of innovization-based representation learning, which aims to discover innovative solution principles from compressed representations. Specifically, a neural net, including an encoder and a decoder, is iteratively learned to transform input solutions into the predictions that dominate them. the encoder thus can capture their compressed representations that retain the information necessary for making good predictions. the decoder can create innovative promising solutions from newly explored representations by evolutionary search. Finally, the proposed algorithm is tested on synthetic and real-world benchmarks with the dimension of variables up to 10(4). Comparison results show the superior performance of the proposed optimizer in solving these benchmarks within 10(5) function evaluations.

关键词： Evolutionary Algorithms Large-Scale Multiobjective optimization Innovization Representation Learning

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A data-driven Algorithm Based on Neural Network Prediction for Agent Trajectory Tracking 5

A Data-Driven Algorithm Based on Neural Network Prediction f...

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

作者： Gu, Junhua Liu, Wei Zheng, Xiaoyuan Liu, Hongpu School of Artificial Intelligence Hebei University of Technology Tianjin China

ISBN: (纸本)9798350393521

A distributed model-free adaptive control scheme for agent systems is presented in this paper, which solves the trajectory tracking problem in fixed communication topology. the unknown nonlinear function is used to model the dynamic equation of each agent. Furthermore, the desired trajectory required is acceptable to a portion of the following agent. the main feature of this method is that it can solve the trajectory tracking control problem only by relying on the I/O data of each agent. A neural net-work is used to estimate the unknown quantity in the adaptive control law. theoretical analysis shows that the tracking error is convergent and the output and control input sequences are bounded. Finally, a simulation is given to verify the effectiveness of the proposed design method. © 2023 IEEE.

关键词： Topology

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Stacking Based LightGBM-CatBoost-RandomForest Algorithm and Its Application in Big data Modeling 4

Stacking Based LightGBM-CatBoost-RandomForest Algorithm and ...

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

作者： Wang, Zhihong Ren, Hongru Lu, Renquan Huang, Lirong Guangdong University of Technology School of Automation Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Recent years, the application of big data model prediction in various fields has been increasing, but the improvement of model accuracy has always been a major problem. Integrating multiple base classifiers by using an ensemble algorithm is an efficient way to improve model accuracy. In this paper, LightGBM, CatBoost and RandomForest are used as base classifiers, and the Stacking method in ensemble learning is used to build a combined model of LightGBM-CatBoost-Random-Forest. A comparative experiment is carried out with the SVM-KNN combination model based on the soft voting method in the existing literature. the results show that the Stacking-based on LightGBM-CatBoost-RandomForest combined model has good performance in the four model evaluation indicators of accuracy, precision, recall and F1 score. © 2022 IEEE.

关键词： Big data

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Model Management Agent for Expensive Constrained Multi-Objective optimization 6

Model Management Agent for Expensive Constrained Multi-Objec...

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6th international conference on data-driven optimization of complex systems (docs)

作者： Wu, Haofeng Ding, Jinhang Liu, Qiqi Jin, Yaochu Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Surrogate-assisted evolutionary algorithms (SAEAs) have been extensively studied for their effectiveness in solving expensive optimization problems in various domains. A critical component of the SAEA framework is model management, which is used to query new solutions and update the surrogate model. this paper introduces a novel model management agent based on reinforcement learning. the developed SAEA, MMA-SAEA, addresses expensive constrained multi-objective optimization problems (ECMOPs). MMA-SAEA conceptualizes the SAEA as an agent that utilizes two distinct model management strategies to achieve global optimization. the first strategy emphasizes objective optimization and constraint satisfaction, while the second strategy balances exploration and exploitation. the performance of MMA-SAEA was evaluated using the benchmark test suites and compared with other state-of-the-art methods. Results indicate that MMA-SAEA delivers superior performance for ECMOPs. In addition, MMA-SAEA offers improved performance on real-world operational indices in beneficiation processes.

关键词： Reinforcement learning agent model management evolutionary algorithms constrainted multi-objective optimization surrogate model

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Solving the Workshop Layout optimization Problem Based on a Constrained Multimodal Multiobjective Evolutionary Algorithm 6

Solving the Workshop Layout Optimization Problem Based on a ...

引用

6th international conference on data-driven optimization of complex systems (docs)

作者： Yue, Caitong Wu, Wenhao Liang, Jing Yu, Kunjie Lin, Hongyu Zhang, Tuo Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Layout optimization (LO) is gaining increasing attention as competition among enterprises intensifies and the demand for cost reduction and efficiency improvement continues to grow. LO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various LO applications, they focus only on the diversity and convergence of the objective space, neglecting the diversity of solutions, which is detrimental to solving potential issues. therefore, this paper proposes a constrained multimodal multiobjective optimization (CMMO) framework based on dual-population multi-task collaborative evolution. this framework assists in solving the workshop layout problem by creating an auxiliary task population and transferring knowledge to support the optimization process. Additionally, the dynamic narrowing of constraint boundaries and gradual expansion of the contrast neighborhood for the auxiliary tasks ensure a high degree of correlation with the main task, continuously providing supplementary evolutionary directions. Ultimately, compared to other constrained multimodal multiobjective algorithms and two classic genetic algorithms, the proposed algorithm proves to be more competitive in practical layout optimization problems.

关键词： Workshop layout optimization evolutionary multitasking auxiliary task

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Prediction Strategy Assisted by LSTM Neural Network for Dynamic Multi-objective optimization 6

Prediction Strategy Assisted by LSTM Neural Network for Dyna...

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6th international conference on data-driven optimization of complex systems (docs)

作者： Li, Suixun Zou, Feng Chen, Debao Huang, Huimei Huaibei Normal Univ Sch Phys & Elect Informat Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Dynamic multi-objective optimization problems (DMOPs) exist widely in real life, and the algorithm needs to track changing Pareto optima accurately and quickly after the environment changes when dealing with these DMOPs. In response to the above issues, this paper proposes a prediction strategy assisted by long short-term memory recurrent neural network (PS-LSTM) for DMOPs. In the proposed method, when environmental changes occur, the difference of the center point of Pareto solutions is predicted by long short-term memory recurrent neural network and then the evolutionary direction of Pareto solutions can be calculated to better track changing Pareto optima by selecting excellent individuals in a new environment. Moreover, the next initial population is predicted based on the difference of the center point and the evolutionary direction of Pareto solutions. Finally, the PS-LSTM strategy is compared with four representative strategies on 11 benchmark test problems. Experimental results indicate that the strategy has better performance.

关键词： Dynamic multi-objective optimization LSTM New environmental information

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