Virtual special issue on advances in meta-heuristics and applications in complex optimisation problems

Introduction: 

 There are various classifications for meta-heuristics, such as evolution-based, swarm intelligence-based, physics-based and human behaviour-based. Examples include simulated annealing (SA), genetic algorithms (GA), particle swarm optimisation (PSO) and differential evolution (DE). Compared with exact algorithms, meta-heuristics depend less on mathematical modelling and derivation – they use the “trial-and-error” principle in searching for solutions. Meta-heuristics with high flexibility show inherent advantages in avoiding local optimum in many cases.

However, finding ways to effectively optimise meta-heuristics remains a challenge.The purpose of this special issue is to bring together the latest theory research on complex optimisation problems.

Topics covered:

These include, but are not limited to:

• Reviews on different meta-heuristics
• Improvements in different meta-heuristics
• Applications of meta-heuristicsin:
  • Logistics and supply chain management
  • Machine learning and deep learning models
  • Engineering optimisation problems
  • Prediction theories andmethods
  • Economics modelling

Important deadlines:

Submission deadline: 15 January 2023

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select Meta-heuristics upon submission.

Guest Editors:

• Dr. Lin Wang, Professor, Huazhong University of Science and Technology, China. Email: wanglin@hust.edu.cn
• Dr. Qinghua Wu, Professor, Huazhong University of Science and Technology, China. Email: qinghuawu1005@gmail.com
• Dr. Wu Deng, Professor, Civil Aviation University of China, China. Email: wdeng@cauc.edu.cn
• Dr. Lu Peng, Associate Research Fellow, Wuhan University of Technology, China. Email: pengluhust@whut.edu.cn

For further enquiries, please send emails to dsm@xjtu.edu.cn, and the editing team will answer promptly.