Guest Editors
Prof. Hongfeng Wang, Northeastern University, China
Prof. Yaping Fu, Qingdao University, China
Prof. Kaizhou Gao, Macau University of Science and Technology, Macau
Prof. Xiwang Guo, New Jersey City University, the US
Summary
To facilitate effective decision-making in real-world applications, meta-heuristics have been enhanced in various ways to successfully address complex optimization problems. However, these meta-heuristics often face significant performance challenges when dealing with large-scale optimization problems. To address this issue, the utilization of machine learning methods is being explored to improve the optimization effectiveness of meta-heuristics. This special issue aims to bring together both researchers and engineers from the academia and industry to discuss emerging and existing issues regarding modeling, optimizing and applying meta-heuristics and machine learning methods in engineering optimization problems. Specially, this issue focuses on the latest developments in swarm and evolutionary algorithms, meta-heuristics, hybridization with machine learning algorithms, and applications in various complex optimization problems. The potential topics include, but are not limited to:
Evolutionary multi-objective optimization with reinforcement learning
Evolutionary multi-task optimization with reinforcement learning
Surrogate-assisted evolutionary computation with learning strategies
Learning-driven evolutionary transfer optimization
Dynamic optimization with ensemble of meta-heuristic and learning methods
Production scheduling problems in manufacturing systems
Energy-efficiency scheduling and optimization problems in industry
Production and distribution optimization in supply chains
Scheduling and optimization problems in sustainability systems
New applications of ensemble with hybridization of meta-heuristics and machine learning algorithms
Keywords
Evolutionary algorithm, Swarm intelligence, Meta-heuristic, Machine learning, Scheduling Optimization
Published Papers
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