Tsinghua Science and Technology

Due to various complexities in real-world complex systems, many optimization problems cannot be solved effectively by traditional methods. Intelligent optimization, including evolutionary computation and swarm intelligence, has been successfully applied to complex systems in a variety of engineering fields. To enhance the optimization capability when solving particular problems, it is very important to incorporate machine learning techniques (such as deep learning techniques) into the intelligent algorithms. Learning-driven optimization is concerned with the use of machine learning techniques in the framework of intelligent optimization. The key issues of learning-driven intelligent optimization include knowledge representation, knowledge learning, knowledge utilization, model management, strategy design, learning mechanism, and the related control scheme. During the past few years, increasing attention has been paid to the theoretical analysis, algorithm design, and performance improvement of the learning-driven optimization as well as a wide range of applications in complex systems. This special issue intends to give the state-of-the-art of learning-driven intelligent optimization for complex systems. It aims to provide a platform for researchers to share innovative work in this area. Interdisciplinary methodologies may be given based on the innovative intelligent optimization and data engineering for complex systems.

The aim of this Special Issue is to reflect the most recent developments of learning-driven optimization techniques. The topics of interest include, but are not limited to

• Knowledge representation, knowledge learning, and knowledge utilization in intelligent optimization
• Reinforcement learning-based intelligent algorithms for complex systems
• Deep learning-based intelligent algorithms for complex systems
• Theoretical analysis learning-driven intelligent optimization
• Intelligent algorithms with knowledge for complex systems
• Applications of learning-driven optimization in complex systems
• Survey of learning-driven optimization for engineering applications

SUBMISSION GUIDELINES

Authors should prepare papers in accordance with the format requirements of Tsinghua Science and Technology, with reference to the Instruction given at https://www.sciopen.com/journal/1007-0214, and submit the complete manuscript through the online manuscript submission system at https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on Learning-Driven Optimization for Complex Systems”.

IMPORTANT DATES

Deadline for submissions: September 30, 2024

GUEST EDITORS

Prof. Ling Wang, Tsinghua University, Beijing, China. Email: wangling@tsinghua.edu.cn

Prof. Wenyin Gong, China University of Geosciences, Wuhan, China. Email: wygong@cug.edu.cn

Prof. Qingfu Zhang, IEEE Fellow, City University of Hong Kong, Hong Kong, China. Email: qingfu.zhang@cityu.edu.hk