A harmony search algorithm for university course timetabling

Abstract

One of the main challenges for university administration is building a timetable for course sessions. This is not just about building a timetable that works, but building one that is as good as possible. In general, course timetabling is the process of assigning given courses to given rooms and timeslots under specific constraints. Harmony search algorithm is a new metaheuristic population-based algorithm, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments together seeking a pleasing harmony. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. In this paper, a harmony search and a modified harmony search algorithm are applied to university course timetabling against standard benchmarks. The results show that the proposed methods are capable of providing viable solutions in comparison to previous works.

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

“…Generally speaking, there are two types of metaheuristic algorithms [10]: local area based algorithms and population-based algorithms. Each type has some advantages and disadvantages.…”

“…Local area based algorithms include simulated annealing 3 [6,58], very large neighborhood search [1,2], TS [9,44], and many more. Usually, local area based algorithms more focus on exploitation rather than exploration [10,21]. They usually work in a non-systematic way that may lead to find a solution in one direction without performing a wider scan of the search space [10,28].…”

“…Usually, local area based algorithms more focus on exploitation rather than exploration [10,21]. They usually work in a non-systematic way that may lead to find a solution in one direction without performing a wider scan of the search space [10,28]. Population-based algorithms start with many solutions and refine them to obtain optimal solution(s) in the whole search space and, hence, are global area based algorithms.…”

“…In that situation , the solving procedure is trapped in the suboptimal state and is unable to generate offspring that are superior to their parents. The main reason for this premature convergence problem in population-based algorithms is that they are more concerned with exploration than exploitation [10]. Population-based algorithms perform search in the whole search space without strictly focusing on the good part of an individual within a population, which may lead to the lose of useful information in a good individual [8].…”

A harmony search algorithm for nurse rostering problems

“…The idea is adapted in the search process for solving optimization problems [19]. HSA can be categorized as a recent evolutionary algorithm and showed to be efficient in solving difficult optimization problems such as university course timetabling [20], vehicle routing [21], Sudoku Puzzle [22], and many others [23][24][25][26][27].…”