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Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications

Submission Deadline: 31 December 2024 Submit to Special Issue

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

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