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Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes

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Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

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Abstract

In this paper, we present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.

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Author information

Authors and Affiliations

  1. ISISTAN Research Institute, UNCPBA University, Campus Universitario, Paraje Arroyo Seco, 7000, Tandil, Argentina

    Virginia Yannibelli & Analía Amandi

  2. CONICET, National Council of Scientific and Technological Research, Buenos Aires, Argentina

    Virginia Yannibelli & Analía Amandi

Authors
  1. Virginia Yannibelli
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  2. Analía Amandi
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Corresponding author

Correspondence to Virginia Yannibelli .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

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Yannibelli, V., Amandi, A. (2015). Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_33

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  • DOI: https://doi.org/10.1007/978-3-319-27060-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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