An evolutionary computational approach for the dynamic Stackelberg competition problems

Authors

  • Lorena Arboleda-Castro Universidad Técnica Estatal de Quevedo
  • Olga Cedeño-Fuentes Universidad Técnica Estatal de Quevedo
  • Iván Jacho-Sánchez Universidad Técnica Estatal de Quevedo
  • Pavel Novoa-Hernández Universidad Técnica Estatal de Quevedo

DOI:

https://doi.org/10.29019/enfoqueute.v7n2.92

Keywords:

Stackelberg competition, evolutionary dynamic optimization, bilevel optimization, metaheuristics

Abstract

Stackelberg competition models are an important family of economical decision problems from game theory, in which the main goal is to find optimal strategies between two competitors taking into account their hierarchy relationship. Although these models have been widely studied in the past, it is important to note that very few works deal with uncertainty scenarios, especially those that vary over time. In this regard, the present research studies this topic and proposes a computational method for solving efficiently dynamic Stackelberg competition models. The computational experiments suggest that the proposed approach is effective for problems of this nature.

Downloads

Download data is not yet available.

References

Blackwell, T. M., & Branke, J. (2006). Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation, 10(4), 459-472.
Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82-117.
Colson, B., Marcotte, P., & Savard, G. (2005). Bilevel programming: A survey. 4OR, 3(2), 87-107.
Colson, B., Marcotte, P., & Savard, G. (2007). An overview of bilevel optimization. Annals of Operations Research, 153(1), 235-256.
Cruz, C., González, J. R., & Pelta, D. A. (2011). Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing, 15(7), 1427-1448.
García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization. J Heuristics, 15, 617-644.
Jin, Y., & Branke, J. (2005). Evolutionary optimization in uncertain environments-a survey. Evolutionary Computation, IEEE Transactions on, 9(3), 303-317.
Legillon, F., Liefooghe, A., & Talbi, E.-G. (2013). CoBRA: A Coevolutionary Metaheuristic for Bi-level Optimization. En E.-G. Talbi (Ed.), Metaheuristics for Bi-level Optimization (Vol. 482, pp. 95-114). Springer Berlin Heidelberg.
León, X., & Navarro, L. (2013). A Stackelberg game to derive the limits of energy savings for the allocation of data center resources. Future Generation Computer Systems, 29(1), 74-83. http://doi.org/http://dx.doi.org/10.1016/j.future.2012.05.022
Meyer-Nieberg, S., & Beyer, H.-G. (2007). Self-Adaptation in Evolutionary Algorithms. En F. Lobo, C. Lima, & Z. Michalewicz (Eds.), Parameter Setting in Evolutionary Algorithms (Vol. 54, pp. 19-46). Springer Berlin / Heidelberg.
Nakamura, T. (2015). One-leader and multiple-follower Stackelberg games with private information. Economics Letters, 127, 27-30. http://doi.org/http://dx.doi.org/10.1016/j.econlet.2014.12.010
Nguyen, T. T., Yang, S., & Branke, J. (2012). Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation, 6(0), 1-24. http://doi.org/http://dx.doi.org/10.1016/j.swevo.2012.05.001
Nie, P. (2012). A note on dynamic Stackelberg games with leaders in turn. Nonlinear Analysis: Real World Applications, 13(1), 85-90. http://doi.org/http://dx.doi.org/10.1016/j.nonrwa.2011.07.015
Novoa-Hernández, P., Corona, C. C., & Pelta, D. A. (2011). Efficient multi-swarm PSO algorithms for dynamic environments. Memetic Computing, 3(3), 163-174.
Novoa-Hernández, P., Corona, C. C., & Pelta, D. A. (2013). Self-adaptive, multipopulation differential evolution in dynamic environments. Soft Computing, 17(10), 1861-1881. http://doi.org/10.1007/s00500-013-1022-x
Novoa-Hernández, P., Cruz Corona, C., & Pelta, D. A. (2016). Self-adaptation in dynamic environments - a survey and open issues. International Journal of Bio-inspired Computation, 8(1), 1-13.
Novoa-Hernández, P., Pelta, D. A., & Corona, C. C. (2010). Improvement strategies for multi-swarm PSO in dynamic environments. En J. González, D. Pelta, C. Cruz, G. Terrazas, & N. Krasnogor (Eds.), Studies in Computational Intelligence (Vol. 284, pp. 371-383). Springer Berlin / Heidelberg.
Oduguwa, V., & Roy, R. (2002). Bi-level optimisation using genetic algorithm. En Artificial Intelligence Systems, 2002. (ICAIS 2002). 2002 IEEE International Conference on (pp. 322-327).
Øksendal, B., Sandal, L., & Ubøe, J. (2013). Stochastic Stackelberg equilibria with applications to time-dependent newsvendor models. Journal of Economic Dynamics and Control, 37(7), 1284-1299. http://doi.org/http://dx.doi.org/10.1016/j.jedc.2013.02.010
Sinha, A., Malo, P., & Deb, K. (2014). Test Problem Construction for Single-Objective Bilevel Optimization. Evolutionary Computation, 22(3), 439-477. http://doi.org/10.1162/EVCO_a_00116
Sinha, A., Malo, P., Frantsev, A., & Deb, K. (2014). Finding optimal strategies in a multi-period multi-leader–follower Stackelberg game using an evolutionary algorithm. Computers & Operations Research, 41, 374-385. http://doi.org/10.1016/j.cor.2013.07.010
Storn, R., & Price, K. (1997). Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341-359. http://doi.org/10.1023/A:1008202821328
Talbi, E.-G. (2013). A Taxonomy of Metaheuristics for Bi-level Optimization. En E.-G. Talbi (Ed.), Metaheuristics for Bi-level Optimization (Vol. 482, pp. 1-39). Springer Berlin Heidelberg.
von Stackelberg, H. (1952). The Theory of the Market Economy. Oxford University Press.
Wahab, O. A., Bentahar, J., Otrok, H., & Mourad, A. (2016). A Stackelberg game for distributed formation of business-driven services communities. Expert Systems with Applications, 45, 359-372. http://doi.org/http://dx.doi.org/10.1016/j.eswa.2015.09.047
Wang, D., Du, G., Jiao, R. J., Wu, R., Yu, J., & Yang, D. (2016). A Stackelberg game theoretic model for optimizing product family architecting with supply chain consideration. International Journal of Production Economics, 172, 1-18. http://doi.org/http://dx.doi.org/10.1016/j.ijpe.2015.11.001

Downloads

Published

2016-06-30

How to Cite

Arboleda-Castro, L., Cedeño-Fuentes, O., Jacho-Sánchez, I., & Novoa-Hernández, P. (2016). An evolutionary computational approach for the dynamic Stackelberg competition problems. Enfoque UTE, 7(2), pp. 10 – 24. https://doi.org/10.29019/enfoqueute.v7n2.92

Issue

Vol. 7 No. 2 (2016)

Section

Miscellaneous

License

The articles and research published by the UTE University are carried out under the Open Access regime in electronic format.  This means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.

The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.

 

This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).

 

The Enfoque UTE journal guarantees and declares that authors always retain all copyrights and full publishing rights without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.