Knowledge- and Learning-Driven Meta-Heuristics for Addressing Complex Optimization and Scheduling Problems

Dear Colleagues,

In the domain of addressing intricate optimization and scheduling challenges across a variety of industry systems, knowledge- and learning-driven meta-heuristics, e.g., genetic algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), and artificial bee colony (ABC), have emerged as powerful tools, offering unparalleled adaptability and computational prowess. This Special Issue delves into the frontier of combining meta-heuristics with problem-specific knowledge and machine learning techniques to cope with the modeling, optimization, and scheduling of engineering optimization problems. Our objective is to explore the latest advancements in meta-heuristics and ensemble methodologies integrated with problem-specific knowledge and machine learning, with a particular focus on their innovative applications in addressing a wide range of optimization and scheduling challenges.

The potential topics include (but are not limited to):

• Swarm intelligence and evolutionary algorithms, e.g., GA, PSO, DE, and ABC, for engineering optimization problems;
• Advanced multi-objective and multi-task optimization with meta-heuristic algorithms;
• Dynamic optimization and adaptive meta-heuristic algorithms;
• Large-scale distributed scheduling and hybrid scheduling according to meta-heuristics;
• Designs of knowledge- and learning-based meta-heuristics for various continuous optimizations;
• Integration of problem knowledge, machine learning, and meta-heuristics in domain-specific applications
  • Production scheduling;
  • Energy-efficiency scheduling;
  • Heath care center scheduling and routing optimization;
  • Traffic signal control, optimization, and scheduling;
  • Vehicle routing problems;
  • Port planning and scheduling;
  • Unmanned vehicles/unmanned surface vessels task assignment and routing planning;
  • Project, grid/cloud, and smart city/building scheduling;
  • Sustainability and green scheduling;
  • Emerging real-world combinatorial optimization.

Prof. Dr. Yaping Fu
Dr. Kaizhou Gao
Prof. Dr. Naiqi Wu
Prof. Dr. Ponnuthurai Nagaratnam Suganthan
Guest Editors

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• swarm intelligence
• evolutionary algorithms
• multi-objective and multi-task optimization
• meta-heuristic algorithms
• dynamic optimization
• machine learning
• production scheduling
• energy-efficiency scheduling
• heath care center scheduling and routing optimization
• traffic signal control, optimization, and scheduling
• vehicle routing problems
• port planning and scheduling
• unmanned vehicles/unmanned surface vessels task assignment and routing planning
• project, grid/cloud, and smart city/building scheduling
• sustainability and green scheduling
• emerging real-world combinatorial optimization