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International Conference on Swarm Intelligence

ICSI 2019: Advances in Swarm Intelligence pp 177–187Cite as

Variants and Parameters Investigations of Particle Swarm Optimisation for Solving Course Timetabling Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11655))

Abstract

University course timetabling problem (UCTP) is well known to be Non-deterministic Polynomial (NP)-hard problem, in which the amount of computational time required to find the optimal solutions increases exponentially with problem size. Solving the UCTP manually with/without course timetabling tool is extremely difficult and time consuming. A particle swarm optimisation based timetabling (PSOT) tool has been developed in order to solve the real-world datasets of the UCTP. The conventional particle swarm optimisation (PSO), the standard particle swarm optimisation (SPSO), and the Maurice Clerc particle swarm optimisation (MCPSO) were embedded in the PSOT program for optimising the desirable objective function. The analysis of variance on the computational results indicated that both main effect and interactions were statistically significant with a 95% confidence interval. The MCPSO outperformed the other variants of PSO for most datasets whilst the computational times required by all variants were moderately difference.

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Acknowledgements

This work was part of research project supported by the Thailand Research Fund (TRF) and Office of the Higher Education Commission (OHEC) under grant number MRG6080066.

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Authors and Affiliations

  1. Faculty of Industrial Technology, Pibulsongkram Rajabhat University, Phitsanulok, 65000, Thailand

    Thatchai Thepphakorn

  2. Centre of Operations Research and Industrial Applications (CORIA), Department of Industrial Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, 65000, Thailand

    Pupong Pongcharoen

Authors
  1. Thatchai Thepphakorn
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  2. Pupong Pongcharoen
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Corresponding author

Correspondence to Pupong Pongcharoen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Thepphakorn, T., Pongcharoen, P. (2019). Variants and Parameters Investigations of Particle Swarm Optimisation for Solving Course Timetabling Problems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-26369-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26368-3

  • Online ISBN: 978-3-030-26369-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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