Special Session on Data-Driven and Neural Network-Aided Dynamic Optimization and Its Applications

Special Session Chairs

• Prof. Wei Song, Jiangnan University, Wuxi, China

     Email: songwei@jiangnan.edu.cn

• Prof. Changhe Li, Anhui University of Science and Technology, Huainan, China

     Email: lichanghe@aust.edu.cn

• A/Prof. Zijia Wang, Guangzhou University, Guangzhou, China

     Email: caizijiaren1111@qq.com

Introduction

With the advent of the digital age and the continuous evolution of intelligent technologies, data-driven and neural network-aided dynamic optimization has emerged as a crucial approach in various fields, such as industrial production, energy management, and transportation systems. Traditional optimization methods often face difficulties in dealing with the high complexity, strong uncertainty, and dynamic changes of modern systems. In contrast, by effectively leveraging real-world data, neural network models can capture complex patterns and relationships in data, enabling more accurate and efficient dynamic optimization.

Neural networks, especially efficient neural networks, have shown remarkable capabilities in handling large-scale and high-dimensional data. They can automatically learn feature representations from data, which is particularly suitable for scenarios where the relationship between input and output is highly non-linear. When combined with real-world data streams, neural network-aided dynamic optimization can adapt to system changes in a timely manner, adjust optimization strategies, and improve overall system performance. Additionally, the integration of advanced data analysis techniques, such as machine learning algorithms and data mining methods, further enriches the means of dynamic optimization, providing more powerful support for decision-making.

This special session aims to bring together researchers, engineers, and industry practitioners to discuss the latest research progress, challenges, and practical applications in the field of data-driven and neural network-aided dynamic optimization. We encourage submissions that cover theoretical research, algorithm design, and real-world case studies, with a particular focus on the integration of neural network models, real-time data processing, and dynamic optimization algorithms.

Keywords

Dynamic Optimization; Dynamic Multiobjective Optimization; Constrained Dynamic Optimization; Constrained Dynamic Multiobjective Optimization; Large-Scale Dynamic Optimization; Neural Networks; Data-driven; Machine Learning; Deep Learning; Optimization Algorithms; Practical Applications

Topics of Interest

Topics include but are not limited to:

• Evolutionary Architecture Search for Large-Scale Neural Networks

• Data-Driven and/or Neural Network-Aided Dynamic Optimization

• Data-Driven and/or Neural Network-Aided Dynamic Multiobjective Optimization

• Data-Driven and/or Neural Network-Aided Constrained Dynamic Optimization

• Data-Driven Constrained Dynamic Multiobjective Optimization

• Neural Network-Based Architecture for Large-Scale Optimization

• Data-Driven Architecture for Large-Scale Dynamic Optimization

• Data-Driven Optimization Framework for Dynamic Route Planning 

• Neural Network-Based Constrained Dynamic Optimization in Resource Allocation

• Industrial Applications of Neural-Network-Based Dynamic Optimization

• Dynamic Multiobjective Optimization in Smart Grids, Robotics, And Autonomous Systems

• Data-Driven Neural Network Architectures for Large-Scale Dynamic Optimization of Transportation Systems