Tsinghua Science and Technology

ISSN 1007-0214 e-ISSN 1878-7606 CN 11-3745/N
Editor-in-Chief: Jiaguang Sun
Open Access journal
Journal Home > Notice List > CFP–Special Issue on Intelligent Optimization and Scheduling with Data & Model Fusion
Release Time:2022-11-28 14:12:32 Views:509
CFP–Special Issue on Intelligent Optimization and Scheduling with Data & Model Fusion

A large number of complex scheduling optimization problems are prevalent in many fields such as defense, manufacturing, energy, transportation, agriculture, and logistics. These problems are characterized by nonlinearity, multi-objective, strong constraints, uncertainty, dynamics, the coexistence of discrete and continuous variables, and time-consuming evaluation, which pose great challenges to traditional optimization methods. Swarm intelligence optimization based on population iterative search has become an effective way to solve complex scheduling optimization problems in recent years because of its simplicity, efficiency, and especially low requirement for problem structure information. The research of efficient and robust intelligent optimization theory and method for complex scheduling problems has become an important hot content in the fields of automation, computer, and management science.

    With the rapid development of cloud computing, big data, and other information technologies, the second generation of artificial intelligence algorithms requires the deep integration of data, knowledge, arithmetic, and algorithms, which also points to the direction of innovation and development of swarm intelligence optimization. Purely model-driven optimization methods suffer from the difficulties of modeling, evaluation, and solution. Traditional swarm intelligence optimization mostly follows the basic steps of population initialization, crossover, mutation, and individual selection. Such purely data-driven optimization methods suffer from an insufficient combination of problem features and slow convergence speed. Therefore, the research on the design of optimization algorithms with data and model fusion can help improve the overall performance of the algorithms and is an important direction for the development of the new generation of population intelligent scheduling optimization.

    Based on the above background, this special issue focuses on the latest theories, methods, and engineering applications of intelligent optimization and scheduling with data and model fusion. We provide a platform for researchers to present new achievements in intelligent scheduling optimization. The topics include, but are not limited to:

  • Knowledge representation and knowledge utilization in intelligent optimization 
  • Reinforcement learning based intelligent algorithms for complex systems 
  • Theoretical analysis knowledge fusion intelligent optimization 
  • Intelligent algorithms with knowledge for multi-objective optimization problems 
  • Intelligent algorithms with knowledge for uncertain optimization problems 
  • Intelligent algorithms with knowledge for simulation optimization problems 
  • Intelligent algorithms with knowledge for constrained optimization problems 
  • Applications of intelligent optimization with knowledge in industry and service systems 
  • Survey of intelligent optimization with knowledge
  • The theory of intelligent scheduling optimization with data and knowledge fusion
  • Intelligent scheduling optimization methods for data and knowledge fusion
  • Multi-objective intelligent scheduling optimization with data and knowledge coalescence
  • Uncertain intelligent scheduling optimization with data and knowledge co-integration
  • Engineering verification and application of intelligent scheduling optimization

SUBMISSION GUIDELINES

Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on Intelligent Optimization and Scheduling with Data & Model Fusion”. Further information on the journal is available at: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5971803.

IMPORTANT DATES

Paper submission: January 1, 2023

Paper submission deadline: August 1, 2023

Expected publication date: December 31, 2023

GUEST EDITORS

Prof. Ling Wang, Department of Automation, Tsinghua University, China. Email: wangling@tsinghua.edu.cn

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

Prof. Rui Wang, College of System Engineering, National University of Defense Technology, China. Email: ruiwangnudt@gmail.com