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An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

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Abstract

Throughout the last few decades, Nature-Inspired Algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler Optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler Optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination; once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well.

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Data availability

Data sets are collected by the UKH from the Korek-telecoms’ HR department. Derived data supporting the findings of this study are available from the corresponding author [Nazir] on request.

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Acknowledgements

The authors would like to thank the University of Kurdistan-Hewler for providing facilities for this research work.

Funding

This study was not funded.

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Authors and Affiliations

  1. Computer Science and Engineering Department, University of Kurdistan Hawler, Erbil, KR, Iraq

    Nazir M. Hasan, Tarik A. Rashid & Ahmed S. Qosaeri

  2. Charles Sturt University, Sydney, Australia

    Abeer Alsadoon

  3. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

  4. Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Zaher Mundher Yaseen

Authors
  1. Nazir M. Hasan
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  2. Tarik A. Rashid
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  4. Ahmed S. Qosaeri
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  5. Laith Abualigah
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  6. Zaher Mundher Yaseen
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Contributions

NMH: concept, software, dataset collection, writing; TAR: concept, writing, editing, supervision; AA: editing; concept: ASQ, LA: editing; ZMY: editing.

Corresponding author

Correspondence to Tarik A. Rashid.

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Hasan, N.M., Rashid, T.A., Alsadoon, A. et al. An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant. Iran J Comput Sci 6, 233–243 (2023). https://doi.org/10.1007/s42044-023-00135-y

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  • DOI: https://doi.org/10.1007/s42044-023-00135-y

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