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检索条件"任意字段=5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023"

共 150 条记录，以下是31-40 订阅

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data-driven Accessibility Public Welfare Communication and Mobile Crowdsourcing System 5

Data-Driven Accessibility Public Welfare Communication and M...

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

作者： Zhao, Tian-Fang Zheng, Shu-Xian Zheng, Qi-Hang Gu, Hong School of Journalism & Communication Jinan University Guangzhou China School of Journalism & Communication Renmin University of China Beijing China

ISBN: (纸本)9798350393521

the level of social accessibility in a city reflects its image and the degree of social civilization. Most existing studies on accessibility public welfare primarily focuses on communication and humanistic perspectives, lacking systematic solutions based on empirical investigations. In this study, we analyze the variables that influence the public's intention to maintain barrier-free facilities by combining the situational theory of publics and the theory of reasoned action. A structural equation model is constructed to comprehend the relationship between barrier-free facility maintenance and public communication. Building upon this, a mobile crowdsourcing system architecture is designed incorporating crowd sensing and implicit crowdsourcing concepts, and the structural equation is refined through practical application. Experimental results indicate a positive correlation between the public's information gaining, involvement, and problem recognition regarding barrier-free issues. Subjective norms, attitudes toward the behavior, and information gaining jointly predict the intention to maintain barrier-free facilities. Additionally, the embedded mobile crowdsourcing system for disseminating barrier-free knowledge and reporting accessibility issues enhances the public's information retrieval on barrier-free concerns. Consequently, this system fosters increased communication and engagement in the realm of barrier-free public welfare, thereby positively influencing the intention to maintain barrier-free facilities. © 2023 IEEE.

关键词： Crowdsourcing

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Improving Multi-objective Evolutionary optimization via Population Distribution Transfer and its Application 6

Improving Multi-objective Evolutionary Optimization via Popu...

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

作者： Xue, Tongxia Mao, Huiting Shi, Xuhua Ningbo Univ Fac Elect Engn & Comp Sci Ningbo Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Multi-objective evolutionary optimization is a class of challenging and complex optimization problems aimed at searching a set of optimal solutions in the face of multiple conflicting objectives. NSGA-III, which is an example of dominance-based algorithms, demonstrates significant capability but struggles with convergence as the number of objectives increases. Here, a multi-objective evolutionary optimization method, named OKTPC, is proposed, which is based on the distribution transfer of populations from single-objective evolutionary optimization. Firstly, the elite population distribution features of single objective problems (SOP) are extracted as knowledge information, and then, the population distribution features of SOP are periodically transferred to the multi-objective problems (MOP) to guide the evolutionary direction in the evolutionary *** prevent SOPs from falling into local optima and causing negative transfer, a SOP evolution strategy combined with a clustering algorithm is designed. Benchmark tests and distillation optimization experiment results demonstrate that the proposed OKTPC can effectively enhance the overall quality of solutions by significantly improving both their convergence speed and maintaining high diversity.

关键词： multi-objective problems knowledge transfer population distribution evolutionary algorithm

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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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A Novel Evolutionary Constrained Multi-Objective optimization Method for Identifying Personalized Drug Targets Combining with Structural Network Control Principles 5

A Novel Evolutionary Constrained Multi-Objective Optimizatio...

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

作者： Liang, Jing Hu, Zhuo Li, Zong-Wei Bi, Ying Cheng, Han Guo, Wei-Feng School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Life Sciences Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350393521

Recently, multi-objective optimization-based structural network control principles (MONCPs) provide a new perspective for identifying personalized drug targets (PDTs), which are envolved with two objectives: minimuming the number of driver nodes (i.e., first objective function) and maximuming the number of prior known drug targets (i.e., second objective function). However, existing methods tend to overlook the knowledge gained from traditional single objective optimization based structural network control principles (SOSNCs, optimizing the first objective function) for solving MONCPs which may affect the accuracy of PDTs idnentification. To improve the performance of MONCPs for identifying PDTs, this paper proposed a novel algorithm called large-scale constrained variables based two stage evolutionary algorithm (LSCV-TSEA) by adapting the prior information of SOSNCs. In the first stage, SOSNCs were utilized in the personalized gene interaction network (PGIN) to explore the information of the first objective function. For the second stage, in the early process the learned information of stage 1 was incorporated into the randomly initialization strategy and during the later evolution process, the search direction of the auxiliary population with optimizing first objective function is modified to the direction of the second objective function, which could help to explore the undeveloped area of PF. By comparing with other algorithms on the three largest cancer omics datasets from the Cancer Genome Atlas database (i.e., breast invasive carcinoma, lung adenocarcinoma and lung squamous cell carcinoma), LSCV-TSEA can more effectively identify PDTs with the higher Area Under the Curve score for predicting clinically annotated combinatorial drugs. Meanwhile, LSCV-TSEA can more efficiently improve the algorithm convergence and diversity compared with other CMOEAs. © 2023 IEEE.

关键词： Evolutionary algorithms

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

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

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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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Comparison of Meta-heuristic optimization Algorithms in Ship Hull Cutting Plan Generation 6

Comparison of Meta-heuristic Optimization Algorithms in Ship...

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

作者： Esfahani, Mahshad Keshtiarast Li, Ming Handroos, Heikki LUT Univ Sch Energy Syst Lappeenranta Finland

ISBN: (纸本)9798350377859;9798350377842

the optimization of ship hull dismantling is crucial for advancing the efficiency and safety of the ship recycling industry, particularly in green facilities. this paper presents a comparative study of meta-heuristic optimization algorithms-Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA)-for generating an optimized cutting plan for end-of-life (EOL) vessels. the fitness function developed aims to minimize the cutting area while imposing constraints on block mass and center of mass displacement, ensuring safe and efficient dismantling. Our study utilized CAD models and Fusion 360 API for data derivation, ensuring precise optimization inputs. Results indicate that GA and SA achieved robust performance, effectively balancing exploration and exploitation, and converging to optimal solutions with cutting areas around 9.1 x 10(6) cm(2). In contrast, PSO struggled with the high-dimensional search space, failing to converge effectively. the GA demonstrated efficiency with a weighted random initialization and tournament selection, while SA benefited from a high initial temperature and an exponential cooling schedule. this study underscores the potential of meta-heuristic algorithms in enhancing the automation and safety of ship dismantling processes.

关键词： Ship recycling meta-heuristic optimization Genetic Algorithm Particle Swarm optimization Simulated Annealing cutting plan environmental safety Fusion 360 API

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A Robot Trajectory Tracking Control Method Based on Spike Neural P System optimization 6

A Robot Trajectory Tracking Control Method Based on Spike Ne...

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

作者： Zeng, Ying Yang, Qiang Tang, Yixuan Hu, Xuefei Hu, Lan Chengdu Univ Informat Technol Coll Automat Chengdu Peoples R China NSMC Ind Technol Inst Biomed Nanchong Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Robots are widely used in many fields and have attracted a lot of attention. this paper proposed a trajectory tracking control method based on spike neural P system optimization, especially for the irregular discrete reference trajectory. Firstly, the kinematics model of the robot is discretized by Euler's forward difference method. Secondly, the trajectory tracking error function is defined, and the trajectory tracking objective function is constructed by using predictive control. Further, in order to overcome the trigonometric coupling problem encountered when solving the predictive controller, An adaptive optimization spiking neural P system is used to solve the objective function and obtain the optimal controller so that the robot can track the desired reference trajectory. Finally, the effectiveness of the proposed method is verified by the simulation of irregular reference trajectories. this paper provides a new solution to the trajectory tracking problem of wheeled mobile robots, especially for irregular discrete reference trajectories.

关键词： Robot Discrete trajectory Trajectory tracking Spike neural P system

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complex Environment Road Object Detection Algorithm Based on Improved YOLOv5s 6

Complex Environment Road Object Detection Algorithm Based on...

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

作者： Da, Hu Gansu Comp Ctr Dept Sci Res Lanzhou 730050 Peoples R China

ISBN: (纸本)9798350377859;9798350377842

A improved YOLOv5s (You Only Look Once 5s) algorithm is intended to dispose of the issues of low detection accuracy, low coverage, and large computational complexity of road object detection algorithms in complex environments. To improve the applicability of the algorithm in various complex scenarios, we draw the CBAM attention mechanism into the CBS module, which is the Backbone benchmark network to enhance the ability to obtain tiny target features and upgrade the recognition rate of tiny targets. By optimizing the CIoU loss function and the alpha IoU loss function, this paper use the alpha IoU loss function to substitute the traditional loss function of YOLOv5s model, this enhancement improves model training accuracy and hasten the convergence rate of the exercise process. the conclusion shows that the improved YOLOv5s algorithm has an accuracy of 94.06%, a recall rate is 92.33%, and an average accuracy is 95.38%. It is superior to other mainstream detection algorithms such as YOLOv5s and SSD, and can achieve road object detection in complex environments. the research results suggest that the improved YOLOv5s algorithm has improved practical efficiency, especially for complex scene object detection, which has important research significance.

关键词： YOLO object detection complex environment algorithm

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Accelerating the Decomposition-based Multiobjective Evolutionary Algorithm via Evolutionary Direction Vector 6

Accelerating the Decomposition-based Multiobjective Evolutio...

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

作者： Zhang, Jingchuan Liu, Yaxue Dong, Zhiming Wang, Xianpeng Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang Peoples R China Northeastern Univ Minist Educ Key Lab Data Analyt & Optimizat Smart Ind Shenyang Peoples R China

ISBN: (纸本)9798350377859;9798350377842

In the process of multi-objective optimization, a large amount of data is generated, which contains the problem-related information. Learning knowledge from this data and applying it to the evolutionary process can improve the quality of the offspring solutions. Most existing knowledge learning methods suffer from high algorithm complexity, and there are still few methods designed specifically for the decomposition-based MOEAs. this paper proposes an evolutionary direction vector (EDV)-guided multiobjective evolutionary algorithm based on decomposition (MOEA/D-EDV). It collects the high-quality evolutionary process data based on the solution's performance in the objective space and their movement directions in the decision space. It then extracts the problem-related knowledge using an efficient learning method to accelerate the evolutionary process. Experimental results on 30 benchmark problems demonstrate that the EDV-guided evolution can significantly accelerate the evolutionary process, and the overall performance of the proposed MOEA/D-EDV is significantly better than the comparative algorithms in the literature.

关键词： multiobjective optimization evolutionary process data knowledge learning evolutionary direction vector

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