An outclassing Multi-objective Hybrid Genetic-based Discrete PSO for Solving the PECT Problem

The Post Enrolment based Course Timetabling (PECT) Problem belongs to, one of the classical problems, the timetabling problems, and it is a part of the most real-life problems that come with multiple constraints of nature. Such a problem is investigated together with both hard and soft constraints, and the solution is an optimal timetable satisfying both constraints as far as possible which reflects the quality of the solution. As a result, there are many approaches to solving the PECT Problem. However most approaches rely upon both the determination of parameters or understanding of domain knowledge. In this research, the Genetic-based Discrete Particle Swarm Optimization (PSO) has been developed with two different local search approaches: Local Search and Tabu Search to solve multi-objective functions and get good solutions by improving the performance of searching solution, which has few parameters to be tuned, and it can outperform all related algorithms from the published work.