Abstract
Shuffled frog leaping (SFL) algorithm is a recently introduced member of memetic algorithms family. It inherits the features of Particle Swarm Optimization and Shuffled Complex Evolution algorithms. Its intensification component of search is similar to Particle Swarm Optimization while the inspiration for diversification is inherited from the global exchange of information in Shuffled Complex Evolution. In this study SFL algorithm is implemented to a discrete problem of human resources distribution as per the present age group to the desired age group distribution. This problem is a challenging part of human resource planning in human resource department of an organization. The simulated results presents that SFL algorithm is able to find optimal adjustment magnitudes of the employees at the selected age groups. The results are also compared with Genetic algorithm.
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References
Drucker, P.F.: The Practice of Management. Harper & Brothers, New York (1954)
Harnpornchai, N., Chakpitak, N., Chandarasupsang, T., Tuang-AthChaikijkosi, Dahal, K.: Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2007), 25–28 September 2007, Singapore, pp. 1234–1239 (2007)
Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plan. Manage. 129(3), 210–225 (2003)
Salomon, R.: Evolutionary algorithms and gradient search: similarities and differences. IEEE Trans. Evol. Comput. 2(2), 45–55 (1998)
Tang, L., Zhao, Y., Liu, J.: An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Trans. Evol. Comput. 18(2), 209–225 (2014)
Dash, R., Dash R., Rautray R.: An evolutionary framework based microarray gene selection and classification approach using binary shuffled frog leaping algorithm. J. King Saud Univ. Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2019.04.002
Pérez-Delgado, M.-L.: Color image quantization using the shuffled-frog leaping algorithm. Eng. Appl. Artif. Intell. 79, 142–158 (2019)
Sharma, T.K., Prakash, D.: Air pollution emissions control using shuffled frog leaping algorithm. Int. J. Syst. Assur. Eng. Manag. (2019). https://doi.org/10.1007/s13198-019-00860-3
Rajpurohit, J., Sharma, T.K., Abraham, A.: Vaishali: glossary of metaheuristic algorithms. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 9, 181–205 (2017)
Eusuff, M.M., Lansey, K.E., Pasha, F.: Shuffled frog-leaping algorithm: a memetic metaheuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)
Coello, C.A.C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Meth. Appl. Mech. Eng. 191, 1245–1287 (2002)
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Sharma, T.K., Abraham, A. (2021). Age Distribution Adjustments in Human Resource Department Using Shuffled Frog Leaping Algorithm. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_61
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DOI: https://doi.org/10.1007/978-3-030-49342-4_61
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