Iterated local search using an add and delete hyper-heuristic for university course timetabling

Abstract

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractHyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated Local Search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyperheuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyperheuristic approach.

Solving university course timetabling problem using localized island model genetic algorithm with dual dynamic migration policy

“…, ILS: Iterative Local Search by Soria‐Alcaraz et al . , HGATS: The Hybrid Approach Hybrid Genetic Algorithm and Tabu search by Jat and Yang , MMA: Mixed Metaheuristic Approach by Cambazard et al . , CTI: Combination of a General Purpose Constraint Satisfaction Solver, Tabu Search and Iterative Local Search Techniques by Atsuta et al .…”

“…UCTP is a minimizing optimization problem, so the objective is to minimize all the predefined constraint violations for each of the teaching events. Accordingly, there are several approaches attempting to solve this complex problem, such as the constraint satisfaction problem , local search , Tabu search , ant colony algorithm , and hybrid algorithms . Therefore, we need a new solution that supports problem scalability and gives a feasible timetable at the same time.…”

A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics

“…Cambazard: the winner of the ICT-2007 competition [24], a multistage local search algorithm considering several neighborhoods Ceschia: a single-step meta-heuristic approach based on simulated annealing, with a neighborhood composed of moves that reschedule one event or swap two events [21] AdapExAP: an adaptive iterated local search hyperheuristics coupled with an adaptive mechanism based on the adaptive pursuit selection rule [9] Goh: Iterative two-stage algorithm that uses tabu search and simulated annealing [25] Nagata: a local search-based algorithm with a mechanism for adapting the size of search neighborhood [26] HHADL: an iterated local search hyper-heuristic with Add-Delete list, which generates heuristics based on a fixed number of add and delete operations [27] HHDMAB: an iterated local search with dynamic multiarm bandits, which selects from a pool of heuristics using an autonomous strategy [10] e main difference between our proposal and other recent methodologies is the application of a categorization process to a predefined set of low-level heuristics. is categorization, detailed in Section 3, leads us to empirical construction of a reasonable good group of heuristics to use as intensification and diversification operators in a selection hyper-heuristic approach.…”

Heuristic Algorithm for Multi-Location Lecture Timetabling

“…Each timetable category consists of different requirements to fulfill that can help in setting the constraints to get a feasible solution. An overview of the timetabling problem-solving approach, such as linear programming [9], constraint-satisfaction strategy, graphcoloring [10], meta-heuristic, and local search algorithms [11], and genetic algorithms [12], [13] are investigated [7], [14].…”