Hybrid Ant Colony systems for course timetabling problems

Abstract

The University Course Timetabling is a complex optimization Problem which is difficult to solve for optimality. It involves assigning lectures to a fixed number of timeslots and rooms; while satisfying some constraints. The goal is to construct a feasible timetable and satisfy soft constraints as much as possible. In this study, we apply two hybrids Ant Colony Systems, namely the Simulated Annealing with Ant Colony System (ACS-SA), and Tabu Search with Ant Colony System (ACS-TS) to solve the university course timetabling, a number of ants in the ACS construct a complete assignment of courses to timeslots. Based on a pre-ordered list of courses, the ants probabilistically choose the timeslot for the given course, guided by heuristic information and stigmergic information. We test both ACS algorithms over the Socha's benchmark course timetabling problem. We also compare our results with those obtained by other methodologies recent literature has illustrated. Experimental results showed that both ACS-SA and ACS-TS produces good quality solutions and outperforms previously applied Ant algorithms; they also outperform other methodologies tested on Socha's benchmark test instances, and approaches on some benchmark instances. We believe that these hybrid ACS algorithms are also valid for other types of combinational optimization problems.

Ant Colony Optimization for a Plan Guide Course Registration Sequence

“…In 2009, Jardat proposed a hybrid algorithm using an Ant Colony system with simulated annealing to solve the problem of university course timetable, which is regarded as a complex organization problem for which it is hard to find an optimal solution [17]. Socha et al (2002) developed a min-max ant system algorithm for the university course time tabling problem.…”

Integrating a Repairing-based Genetic Algorithm-Neighborhood Search Structure in Solving the Course Timetabling Problem

“…The approaches that are commonly undertaken to solve the problems are metaheuristic algorithms which can be classified to population based algorithms such as Genetic Algorithm (Abdelhalim and El Khayat, 2016), Particle Swarm Optimization (Kennedy and Eberhart, 1995), Ant Colony Optimization (Socha et al, 2003) and local search algorithms such as Simulated Annealing (Bellio et al, 2016), Tabu Search (Lü and Hao, 2010), Great Deluge (Dueck, 1993) and Variable Neighborhood Search (Hansen and Mladenović, 1997) to name a few. The aforementioned algorithms possess their own sets of strength and weaknesses and in order to obtain a high quality solution, hybrid algorithms are proposed in order for the resultant algorithm to exhibit various strength derived from the initial algorithms such as hybrid cat swarm algorithms (Skoullis et al, 2016), hybrid particle swarm optimization (Shiau, 2011), hybrid ant colony systems (Ayob and Jaradat, 2009). This paper presents a hybrid Genetic Algorithm Neighborhood Search which integrates domain-specific exploitative properties of the Neighborhood Search into Genetic Algorithm to solve the CTP adopted from a real world example from a faculty in Universiti Teknologi Malaysia.…”

Low-level Teamwork Hybridization for P-metaheuristics: A review and comparison

“…In one research work, this technique has demonstrated an excellent convergence when tested for uncovering low risk paths or routes in a sparse graph [40]. Hybridization between ACO with simulated annealing and ACO with tabu search with related to the course timetabling problem has produced good solutions compared to others methodologies [41]. In EAs, using S-metaheuristics strategy as a selection method has increased the algorithm performances in solving optimization problems.…”