Abstract
This paper describes proposal for the application to modify the Ant Colony Optimization for multiobjective optimization non-compensation model problem staff selection. After analyzing the combinatorial problem involving multicriterial process of recruitment and selection model, it proposed non-compensating its solution using the modified ACO heuristic strategy. This shows that the lack of opportunities to receive appropriate the resulting matrix is related to the accurate prediction of the decision at an acceptable as satisfactory for implementation only available deterministic algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lewicki, A.: Use the ant colony optimization algorithms to build the decision-making multicriterion system for recruitment and selection of employees. In: Dissertation, AGH, Krakow (2009)
Lewicki, A., Tadeusiewicz, R.: The recruitment and selection of staff problem with an Ant Colony System, Backgrounds and Applications. In: Advances in Intelligent and Soft Computing, vol. 2. Springer, Heidelberg (2010)
Jassim, R.K.: Competitive Advantage through the Employees, CCH, Australia (2007)
Yakubovich, V.: Stages of the Recruitment Process and the Referrer’s Performance Effect, Informs, Maryland (2006)
Dorigo, M., Socha, K.: An introduction to ant colony optimization. Technical Report TR/IRIDIA/2006-010 (2006)
Decastro, L., Von Zuben, F.: Recent Developments In Biologically Inspired Computing. Idea Group Publishing, Hershey (2004)
Azzag, H., Monmarché, N., Slimane, M., Venturini, G., Guinot, C.: AntTree: A new model for clustering with artificial ants. In: IEEE Congress on Evolutionary Computation, Canberra. wolumen, vol. 4, pp. 2642–2647. IEEE Press, Los Alamitos (2003)
Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artificial Life 12(1) (2005)
Pang-Ning, T.: Introduction to Data Mining. Addison Wesley Publication, Reading (2006)
Sendova-Franks, A.: Brood sorting by ants: two phases and differential diffusion. Animal Behaviour (2004)
Abbass, H.A., Hoai, N.X., McKay, R.I.: AntTAG.: A new method to compose computer using colonies of ants. In: Proceedings of the IEEE Congress on Evolutianory Computation, Honolulu, vol. 2 (2002)
Dowsland, K., Thompson, J.: Ant colony optimization for the examination scheduling problem. Journal of the Operational Research Society, 426–439 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tadeusiewicz, R., Lewicki, A. (2010). The Ant Colony Optimization Algorithm for Multiobjective Optimization Non-compensation Model Problem Staff Selection. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-16493-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
eBook Packages: Computer ScienceComputer Science (R0)