Abstract
Literature reveals that optimization algorithms are generally composed of a large number of parameters that highly influence on its performance. In the early stages of the definition of a new algorithm, it is crucial to know how the uncertainty in the input parameters affects the behavior of the algorithm, influencing on its final output, so that it is possible to set up the most efficient configuration.
In this work, we are making a sensitivity analysis using the Extended Fourier Amplitude Sensitivity Test to compute the first order effects and interactions for each parameter on a recently proposed particle swarm optimization algorithm that implements a dynamic structured swarm, based on coalitions. This technique, inherited from game theory, includes four new parameters that are analyzed and tested on a well-known benchmark for continuous optimization. Results give interesting insights of the importance of one of the parameters over the rest.
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
Similar content being viewed by others
References
Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Operations Research/Compuer Science Interfaces. Springer, Heidelberg (2008). https://doi.org/10.1007/978-0-387-77610-1
Dorronsoro, B., Bouvry, P.: Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Trans. Evol. Comput. 15(1), 67–98 (2011)
Dorronsoro, B., Bouvry, P.: Cellular genetic algorithms without additional parameters. J. Supercomputing 63(3), 816–835 (2013)
Dorronsoro, B., Burguillo, J.C., Peleteiro, A., Bouvry, P.: Evolutionary algorithms based on game theory and cellular automata with coalitions. In: Zelinka, I., Snášel, V., Abraham, A. (eds.) Handbook of Optimization. Intelligent Systems Reference Library, vol. 38, pp. 481–503. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30504-7_19
Ruiz, P., Dorronsoro, B., Torre, J., Burguillo, J.: Including dynamic adaptative topology to particle swarm optimization algorithms. In: Proceedings of the 21 Congreso Int. de Dirección e Ingeniería de Proyectos. Lecture Notes in Management and Industrial Engineering. Springer (2018, in press)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Clerc, M.: Particle Swarm Optimization. ISTE (International Scientific and Technical Encyclopedia) (2006)
Saltelli, A., Tarantola, S., Chan, K.P.S.: A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41(1), 39–56 (1999)
Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley (2004)
Iooss, B., Lemaître, P.: A review on global sensitivity analysis methods. In: Dellino, G., Meloni, C. (eds.) Uncertainty Management in Simulation-Optimization of Complex Systems. ORSIS, vol. 59, pp. 101–122. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7547-8_5
R Core Team: R: a language and environment for statistical computing. https://www.R-project.org. Accessed 2017
Etxeberria, L., Trubiani, C., Cortellessa, V., Sagardui, G.: Performance-based selection of software and hardware features under parameter uncertainty. In: Proceedings of the 10th International ACM SIGSOFT Conference on Quality of Software Architectures, pp. 23–32 (2014)
Srinivas, C., Reddy, B.R., Ramji, K., Naveen, R.: Sensitivity analysis to determine the parameters of genetic algorithm for machine layout. Procedia Mater. Sci. 6(Icmpc), 866–876 (2014)
Loubière, P., Jourdan, A., Siarry, P., Chelouah, R.: A modified sensitivity analysis method for driving a multidimensional search in the artificial bee colony algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1453–1460 (2016)
Pinel, F., Danoy, G., Bouvry, P.: Evolutionary algorithm parameter tuning with sensitivity analysis. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds.) SIIS 2011. LNCS, vol. 7053, pp. 204–216. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-25261-7_16
Iturriaga, S., Ruiz, P., Nesmachnow, S., Dorronsoro, B., Bouvry, P.: A parallel multi-objective local search for AEDB protocol tuning. Proceedings of the IEEE 27th International Parallel and Distributed Processing Symposium Workshops and Ph.D. Forum, IPDPSW 2013 (Section VI), pp. 415–424 (2013)
Auder, B., Crécy, A., Iooss, B., Marqués, M.: Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations. Reliab. Eng. Syst. Safety 107, 122–131 (2012)
Hamby, D.M.: A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assess. 32, 135–154 (1994)
Lefebvre, S., Roblin, A., Varet, S., Durand, G.: A methodological approach for statistical evaluation of aircraft infrared signature. Reliab. Eng. Syst. Safety 95, 484–493 (2010)
Teodoro, G., Kurç, T., Taveira, L., Melo, A., Gao, Y., Kong, J., Saltz, J.: Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines. Bioinformatics 33(7), 1064–1072 (2017)
Li, X.: Improving multi-agent coalition formation in complex environments. Ph.D. thesis, University of Nebraska (2007)
Liang, J.J., Qu, B.Y., Suganthan, P.N., Chen, Q.: Learning-based real-parameter single objective optimization. In: IEEE Congress on Evolutionary Computation, Nanyang Technological University, Singapore (2015)
Acknowledgment
The authors would like to acknowledge the Spanish MINECO and ERDF for the support provided under contracts TIN2014-60844-R (the SAVANT project) and RYC-2013-13355.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ruiz, P., Dorronsoro, B., de la Torre, J.C., Burguillo, J.C. (2018). Finding the Most Influential Parameters of Coalitions in a PSO-CO Algorithm. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-91479-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91478-7
Online ISBN: 978-3-319-91479-4
eBook Packages: Computer ScienceComputer Science (R0)