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.
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
Submission deadline: 15 January 2023
- Dr. Lin Wang, Professor, Huazhong University of Science and Technology, China. Email: email@example.com
- Dr. Qinghua Wu, Professor, Huazhong University of Science and Technology, China. Email: firstname.lastname@example.org
- Dr. Wu Deng, Professor, Civil Aviation University of China, China. Email: email@example.com
- Dr. Lu Peng, Associate Research Fellow, Wuhan University of Technology, China. Email: firstname.lastname@example.org
For further enquiries, please send emails to email@example.com, and the editing team will answer promptly.