University Course Scheduling using Prominent Nature Inspired Techniques

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, two novel PSO and Group Search Optimizer (GSO) based methods are investigated for solving highly constrained UCSP in which basic PSO and GSO operations are transformed to tackle combinatorial optimization task of UCSP and a few new operations are introduced to PSO and GSO to solve UCSP efficiently. In the proposed methods, swap sequence-based velocity and movement computation and its application are developed to transform individual particles and members in order to improve them. Selective search and forceful swap operation with repair mechanism are the additional new operations in the proposed methods for updating particles and members with calculated swap sequences. The proposed PSO with selective search (PSOSS) and GSO with selective search (GSOSS) methods have been tested on an instance of UCSP resembling the course structure of 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 methods compared to other prominent metaheuristic methods (e.g., GA, HS).