Optimization of University Course Scheduling Problem using Particle Swarm Optimization with Selective Search

Abstract

The University Course Scheduling Problem (UCSP) is a highly constrained real-world combinatorial optimization task. Solving UCSP means creating an optimal course schedule by assigning courses to specific rooms, instructors, students, and timeslots by taking into account the given constraints. Several studies have reported different metaheuristic approaches for solving UCSP including Genetic Algorithm (GA) and Harmony Search (HS) algorithm. Various Swarm Intelligence (SI) optimization methods have also been investigated for UCSP in recent times and a few Particle Swarm Optimization (PSO) based methods among them with different adaptations are shown to be effective. In this study, a novel PSO-based method is investigated for solving highly constrained UCSP in which basic PSO operations are transformed to tackle combinatorial optimization task of UCSP and a few new operations are introduced to PSO to solve UCSP efficiently. In the proposed method, swap sequence-based velocity computation and its application are developed to transform individual particles in order to improve them. Selective search and forceful swap operation with repair mechanism are the additional new operations in the proposed method for updating particles with calculated swap sequences as velocities. The proposed PSO with selective search (PSOSS) method has been tested on an instance of UCSP for the Computer Science and Engineering Department of Khulna University of Engineering & Technology which has many hard and soft constraints. Experimental results revealed the effectiveness and the superiority of the proposed method compared to other prominent metaheuristic methods (e.g., GA, HS).