A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems

Zhang, Defu; Liu, Yongkai; M’Hallah, Rym; Leung, Stephen C. H.

doi:10.1016/j.ejor.2009.09.014

Cited by 82 publications

(54 citation statements)

References 33 publications

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“…The average execution time as well as the respective standard deviation is presented, too. In Table 4 the best performance of both proposed algorithms is compared with the best timetables created by the evolutionary algorithm (EA) presented in [25], the simulated annealing (SA) algorithm presented in [19], the hybrid PSO algorithm presented in [20] and the genetic algorithm selection perturbative hyper-heuristic (GASPHH) presented in [26].…”

Section: Computational Resultsmentioning

confidence: 99%

“…In order to demonstrate the performance and efficiency of both computational intelligence algorithms, their experimental results are compared with the respective results of four different heuristics that have been applied to the school timetabling problem in the literature [19,20,25,26]. The approach presented in [19] is a simulated annealing (SA)-based algorithm with a newly-designed neighborhood structure. Its main innovation is that, in search for the best neighbor, the heuristic performs a sequence of swaps between pairs of timeslots, instead of swapping two assignments, as in the standard simulated annealing.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The reason why we decided to present and compare the best timetables constructed by the proposed algorithms is that in [19,20,25,26] also the best timetables constructed are presented. Thus, in this way a fair comparison between all algorithms can be performed.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The specific input instances used for their performance evaluation and comparison is the well-established Beligiannis benchmark [7]. This school timetabling data set has been already used as a benchmark by many researchers in the respective literature [6,7,18,19].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Modern Heuristics on the School Timetabling Problem

2015

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Abstract:In this contribution a comparative study of modern heuristics on the school timetabling problem is presented. More precisely, we investigate the application of two population-based algorithms, namely a Particle Swarm Optimization (PSO) and an Artificial Fish Swarm (AFS), on the high school timetabling problem. In order to demonstrate their efficiency and performance, experiments with real-world input data have been performed. Both algorithms proposed manage to create feasible and efficient high school timetables, thus fulfilling adequately the timetabling needs of the respective high schools. Computational results demonstrate that both algorithms manage to reach efficient solutions, most of the times better than existing approaches applied to the same school timetabling input instances using the same evaluation criteria.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Modern Heuristics on the School Timetabling Problem

2015

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“…/j.jag.2011 two neighbouring solutions and from Zhang et al (2010) in order to get a rejection rate of about 5-10 %.…”

Section: Simulated Annealing Decreasing Temperature Lawmentioning

confidence: 99%

A spatial stochastic algorithm to reconstruct artificial drainage networks from incomplete network delineations

Bailly

Levavasseur²,

Lagacherie³

2011

International Journal of Applied Earth Observation and Geoinfor

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A spatial stochastic algorithm that aims to reconstruct an entire artificial drainage network of a cultivated landscape from disconnected reaches of the network is proposed here. This algorithm uses random network initialisation and a simulated annealing algorithm, both of which are based on random pruning or branching processes, to converge the multi-objective properties of the networks; the reconstructed networks are directed tree graphs, conform to a given cumulative length and maximise the proportion of reconnected reaches. This algorithm runs within a directed plot boundaries lattice, with the direction governed by elevation. The proposed algorithm was applied to a 2.6-km 2 catchment of a Languedocian vineyard in the south of France.The 24-km-long reconstructed networks maximised the reconnection of the reaches obtained either from a hydrographic database or remote sensing data processing. The distribution of the reconstructed networks compared to the actual networks was determined using specific topographical and topological metrics on the networks. The results show that adding data on disconnected * Corresponding author Email addresses: bailly@teledetection.fr (J.S. Bailly), levavasseur@supagro.inra.fr (F. Levavasseur), lagacherie@supagro.inra.fr (P. Lagacherie) Preprint submitted to International Journal of Applied Earth Observation and GeoinformationJune 7, 2011Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(6) Doi : 10.1016Doi : 10. /j.jag.2011 reaches to constrain reconstruction, while increasing the accuracy of the reconstructed network topology, also adds biases to the geometry and topography of the reconstructed network. This network reconstruction method allows the mapping of uncertainties in the representation while integrating most of the available knowledge about the networks, including local data and global characteristics. It also permits the assessment of the benefits of the remote sensing partial detection process in drainage network mapping., 853-862. The original publication is available at http://www.sciencedirect.com/

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Effective Application of Simulated Annealing

Ohlmann

Laarhoven

2011

Wiley Encyclopedia of Operations Research and Management Science

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First adapted as a solution approach for combinatorial optimization problems in the 1980s, simulated annealing and its variants continue to enjoy widespread application. The widespread application of SA is due to the relative ease of implementing its logic, its general nature and flexibility allowing use with little problem‐specific tailoring (at least for alpha versions of an implementation), and its robust performance with respect to solution quality and running time on a wide array of problems. This article describes implementation details for various features of the simulated annealing algorithm, and then illustrates the development of annealing‐based algorithms through the description of several applications.

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A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems

Cited by 82 publications

References 33 publications

A Comparative Study of Modern Heuristics on the School Timetabling Problem

A Comparative Study of Modern Heuristics on the School Timetabling Problem

A spatial stochastic algorithm to reconstruct artificial drainage networks from incomplete network delineations

Effective Application of Simulated Annealing

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