Comparison of genetic algorithm with Particle Swarm Optimisation, ant colony optimisation and Tabu search based on university course scheduling system

Abstract

Objectives: Planning and allocation of the various resources according to the constraints is a hilarious task. The paper aims to find a suitable method to solve the university course scheduling problem. Methods and Statistical Analysis: This paper compares the usage of Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Tabu Search and Genetic Algorithm (GA) in the preparation of University Course Scheduling System. Certain hard constraints, which has to be satisfied and some soft constraints that can be satisfied are considered. Findings: The algorithm should check for the satisfaction of the hard constraints and the possibility of satisfying the soft constraints. Application/Improvements: The performance of the suitable method is found by comparing with the other methods based on various parameters.