Comparison of Metaheuristic Algorithms with a Methodology of Design for the Evaluation of Hard Constraints over the Course Timetabling Problem

Jorge, A Soria-Alcaraz; Carpío, Martín; Soberanes, Héctor José Puga; Sotelo-Figueroa, Marco Aurelio

doi:10.1007/978-3-642-33021-6_23

Cited by 12 publications

(12 citation statements)

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“…For each instance 3 lists (MMA, LPH and LPA) are built. The detailed construction process for each of these lists is beyond the scope and purpose of this article, but interested readers are referred to [7,44], In the same manner as discussed above, these lists ensure by design that every 3-tuple (e, t, p) represent a feasible selection in terms of time-space constraints. The main optimization exercise is to minimize the student conflict by means of the permutation of the events into timeslots (i.e.…”

Section: Combining Methodology Of Design With Parallelmentioning

confidence: 99%

“…This paper takes parallel metaheuristics from the state of the art and applies them to ITC2007 instances. The methodology of design approach adopted [7] responds to the necessity of generic solution schemes [4], applicable to real world instances [28,29]. The main objective of this effort is to yield state of the art metaheuristics that can be directly applicable to a wide range of real CTTP instances without losing time and resources in building instance-specific algorithms.…”

Section: Problem Definitionmentioning

confidence: 99%

“…All of the parallel algorithms proposed in this paper utilize a generic representation named methodology of design [7]. This representation factors out commonality the solution of different CTTP instances via the identification of generic structure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallel Meta-heuristic Approaches to the Course Timetabling Problem

Soria-Alcaraz

Carpío

Soberanes

et al. 2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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The Course Timetabling problem is one of the most difficult combinatorial problems that arises with a University. The main objective of this problem is to obtain a timetable that minimises student conflicts between assigned activities. This is a discrete combinatorial problem that can be extremely difficult to solve for a human expert so computational heuristics are usually implemented in order to find good solutions within a reasonable time. With the advent of multi-core and hyperthreading technologies, parallel heuristics can speed up the solution process and with a proper parallel design these heuristics can improve the quality of solutions with the same number of Fitness evaluations than sequential algorithms. This paper explores the implementation of a parallel set of heuristic algorithms based on genetic algorithms, Scatter Search and discrete PSO for CTTP problem. Our experiments used as benchmark set instances from ITC2007 Track 2. Also the algorithms described in this paper make use of a layer of independence called methodology of design in order to be easily adaptable to new instances. Every parallel algorithm is compared against its sequential counterpart through speed-up metrics like Weak speed-up proposed by Alba et al.

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Section: Combining Methodology Of Design With Parallelmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Meta-heuristic Approaches to the Course Timetabling Problem

Soria-Alcaraz

Carpío

Soberanes

et al. 2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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“…In order to deal with a given university course timetabling problem in a generic fashion (including instance-specific constraints) as provided in Section 2.2, the methodology of design developed by Soria-Alcaraz et al [53,54] is utilised. The main idea of this methodology is to add an extra layer of generality, the principal objective of which is to discard by design the largest number of instance-specific constraints, in order to build a search space where the heuristic strategy deals with a minimum number of constraints.…”

Section: Methodology Of Design For University Course Timetablingmentioning

confidence: 99%

“…The idea is to construct similar structures for each event in order to have a feasibility region for each possible subject-timeslot assignment. A detailed analysis of the construction of these structures in more complex scenarios can be found in Soria-Alcaraz et al [53,54]. For the soft constraints, our ADL hyper-heuristic searches in the feasible space (by means of the LP H × LP A list) for the elements that satisfy the largest number of time-related criteria.…”

Section: Methodology Of Design For University Course Timetablingmentioning

confidence: 99%

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

Soria-Alcaraz

Özcan

Swan

et al. 2016

Applied Soft Computing

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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.

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