## 5 Results
### Topics
* **Topic 2. **_Vehicle Routing and Logistics_ This topic uses hyper-heuristics to tackle vehicle routing problems, focusing on environmental and operational constraints. Prominent subtopics include green vehicle routing, simultaneous pickup and delivery, and heterogeneous fleets. The literature emphasizes cost and carbon footprint reduction, traffic considerations, and real-world logistics constraints. Key studies propose hybrid metaheuristics and reinforcement learning strategies tailored to dynamic routing scenarios, suggesting a trend toward sustainable and adaptive transport logistics optimization. * **Topic 3. **_Cloud Computing and Resource Management_ Research on this topic targets the optimization of cloud-based systems through hyper-heuristics that manage computing resources, energy consumption, and task scheduling. Articles highlight applications in scientific workflow scheduling and multi-objective cost management in cloud environments. The field increasingly integrates low-level scheduling heuristics with optimization methods to improve scalability and efficiency in distributed computing infrastructures. * **Topic 4. **_Machine Learning and Data Mining_ This topic integrates hyper-heuristics with machine learning tasks, such as feature selection, classification, and cyber-security analytics. Techniques like support vector machines, decision trees, and ensemble learning are frequently used. Representative studies focus on optimizing algorithm selection or parameter tuning to improve model accuracy and robustness, particularly for large-scale datasets. * **Topic 5. **_Educational Timetabling_ This topic encompasses the development of hyper-heuristics to solve educational timetabling problems. Key contributions include iterative local search strategies, hybrid methods combining add-delete mechanisms, and selection hyper-heuristics tuned for institutional constraints. The articles emphasize real-world applications, including school and university timetabling, incorporating hard and soft constraint handling, and addressing scalability and robustness in schedule generation. * **Topic 6. **_Traveling Salesman Problem_ This topic explores hyper-heuristic approaches for solving variants of the traveling salesman problem, including multi-depot and modified cost functions. This line of research emphasizes generating adaptive heuristics for pathfinding tasks under various constraints and problem sizes. * **Topic 7. **_Evolutionary Computation for Routing Problems_ This topic highlights the application of evolutionary algorithms, especially genetic programming and co-evolutionary strategies, to solve routing problems characterized by uncertainty and capacity constraints. Recent years have focused on capacitated arc routing problems and real-time path planning under uncertain demand or environmental conditions. Representative studies explore the interaction between reactive and predictive routing elements, indicating a shift toward hyper-heuristic systems that adapt to dynamic and stochastic problem instances. * **Topic 8. **_Healthcare Scheduling and Resource Allocation_ This topic addresses the scheduling of healthcare personnel and resources, particularly nurse rostering and patient care coordination. Hyper-heuristics in this context aim to satisfy complex institutional requirements such as coverage, shift preferences, and workload balancing. The integration of domain-specific knowledge, such as multi-stage rostering models and hidden Markov models, shows how hyper-heuristics are tailored to the unique constraints of healthcare systems, emphasizing both efficiency and fairness. * **Topic 9**. _Unmanned Aerial Vehicle (UAV) Path Planning and Optimization_ This emerging topic covers UAV mission planning through hyper-heuristic approaches, focusing on multi-objective optimization for coverage, surveillance, and communication tasks. Research emphasizes clustering, swarm intelligence, and real-time path adjustment in uncertain environments to optimize autonomous flight operations. * **Topic 10**.