Application of Ant Colony algorithm in music teaching resource scheduling and optimization

Effective allocation of resources, such as staff, classrooms and instruments, for music education is vital to improve learning and productivity. The characteristics of music education are multiple constraints and dynamism, which the current method cannot be solved. Current methods tend to generate a lot of conflict and a low utilisation of resources and do not cater for learning needs. In this study, the authors suggest a hybrid model called Ant Colony Optimization and Constraint Based Scheduling (ACO-CS) based on a combinatorial model for scheduling to overcome the above problems. ACO-CS dynamically allocates resources without conflicts by searching using pheromone and applying constraint-based rules to enhance capacity of resources. The approach was applied to a number of different-sized scheduling problems in music education that were simulated. It led to 0.8 conflicts/session, an efficiency of resource utilization of 89.6% and an average computational time of 2.9 s for 1000 scheduling sessions. Also, all evaluation metrics reported, such as Learning Outcome Rate (simulated LOR), Simulated Teaching Efficiency Ratio (TER), Student Synthetic Autonomy Index, Simulated Music Performance Score, are model-derived simulation based measures of scheduling performance and not pedagogical or student learning outcomes. When the parameters were simulated under control conditions, simulated LOR was obtained of about 89%, Simulated TER was observed at 1.15, and simulated music performance score was obtained at 92%. The method will show a scalable and efficient solution to schedule music resources in a synthetic constraint environment.