A hybrid particle swarm optimization based algorithm for high school timetabling problems

Tassopoulos, Ioannis X.; Beligiannis, Grigorios N.

doi:10.1016/j.asoc.2012.05.029

Cited by 54 publications

(76 citation statements)

References 16 publications

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“…The PSO algorithm presented in current contribution is based on the PSO algorithm introduced in [20]. However, the proposed algorithm has significant differences compared to that algorithm, which are the following (see Section 3.1 for more details):…”

Section: Introductionmentioning

confidence: 99%

“…• The population size of the PSO based algorithm used in current work equals 15, while in [20] equals to 50.…”

Section: Introductionmentioning

confidence: 99%

“…• In [20] procedure SwapWithProbability() accepts swaps causing a hard clash with probability 2.2%, while in current algorithm this probability is set to 50%.…”

Section: Introductionmentioning

confidence: 99%

“…• In the proposed PSO algorithm procedure SwapWithProbability() accepts swaps which cause a raise in the individual's fitness with probability equal to 0.5% while in [20] this probability is set to 2.2%.…”

Section: Introductionmentioning

confidence: 99%

“…• In the proposed PSO algorithm procedure SwapWithProbability() is applied to two randomly selected timeslots, while in [20] one of the two timeslots selected should have a hard clash (if such a timeslot exists) .…”

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: Introductionmentioning

confidence: 99%

“…• The population size of the PSO based algorithm used in current work equals 15, while in [20] equals to 50.…”

Section: Introductionmentioning

confidence: 99%

“…• In [20] procedure SwapWithProbability() accepts swaps causing a hard clash with probability 2.2%, while in current algorithm this probability is set to 50%.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations