Swarm Intelligence Framework using Hybrid ACO–PSO for Lecture Scheduling in Higher Education

Complex combinatorial optimization problems that must meet various hard constraints and soft constraints occur in lecture scheduling. A feasible and high-quality schedule in limited computing time is often difficult to produce using conventional methods. In this study, a hybrid optimization model is proposed that combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), the aim of which is to improve solution quality and convergence speed. In this model, ACO builds solutions based on pheromone intensity and heuristic information, while PSO is used to dynamically adjust ACO parameters through learning from individual and global search experiences. The model is implemented using MATLAB R2023b and tested on real data involving 10 courses, 4 classrooms, and 6 time slots per day. The ACO+PSO approach is significantly able to reduce the penalty value. This approach reflects better fulfillment of constraints and is the result of experiments obtained. Compared to pure ACO, the hybrid method shows more consistent and stable performance in various trials. Visualization of parameter convergence also strengthens the effectiveness of this hybrid approach in finding the optimal parameter configuration. This research contributes to the development of an intelligent lecture scheduling system that is adaptive and aligned with institutional policies.