Abstract
In this paper we propose an evolutionary approach in order to infer the values of the parameters for applying the MURAME, a multicriteria method which allows to score/rank a set of alternatives according to a set of evaluation criteria. This problem, known as preference disaggregation, consists in finding the MURAME parameter values that minimize the inconsistency between the model obtained with those parameters and the true preference model on the basis of a reference set of decisions of the Decision Maker. In order to represent a measure of inconsistency of the MURAME model compared to the true preference one, we consider a fitness function which puts emphasis on the distance between the scoring of the alternatives given by the Decision Maker and the one determined by the MURAME. The problem of finding a numerical solution of the involved mathematical programming problem is tackled by using an evolutionary solution algorithm based on the Particle Swarm Optimization. An application is finally provided in order to give an initial assessment of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Belacel, N., Bhasker Raval, H., Punnen, A.P.: Learning multicriteria fuzzy classification method. PROAFTN from data. Computers & Operations Research 34(7), 1885–1898 (2007)
Blackwell, T., Kennedy, J., Poli, R.: Particle swarm optimization – An overview. Swarm Intelligence 1(1), 33–57 (2007)
Bonabeau, E., Dorigo, M., Theraulaz, G.: From Natural to Artificial Swarm Intelligence. Oxford University Press (1999)
Brans, J.P., Vincke, P.: A preference ranking organisation method (The PROMETHEE method for multiple criteria decision-making). Management Science 31(6), 647–656 (1985)
Buchanan, J., Sheppard, P., Vanderpooten, D.: Project ranking using ELECTRE III. Research report 99-01, Department of Management Systems, University of Waikato, New-Zealand (1999)
Corazza, M., Fasano, G., Gusso, R.: Portfolio selection with an alternative measure of risk: Computational performances of particle swarm optimization and genetic algorithms. In: Perna, C., Sibillo, M. (eds.) Mathematical and Statistical Methods for Actuarial Sciences and Finance, pp. 123–130. Springer (2012)
Corazza, M., Funari, S., Gusso, R.: Il merito creditizio delle Pmi italiane durante la crisi finanziaria: l’utilizzo di più fonti informative per l’analisi e lo scoring. Bancaria 1(1), 47–63 (2012) (in Italian)
Cura, T.: Particle swarm optimization approach to portfolio optimization. Nonlinear Analysis: Real World Applications 10(4), 2396–2406 (2009)
Doumpos, M., Marinakis, Y., Marinaki, M., Zopounidis, C.: An evolutionary approach to construction of outranking models for multicriteria classification: The case of the ELECTRE TRI method. European Journal of Operational Research 199(2), 496–505 (2009)
Fletcher, R.: Practical Methods of Optimization. John Wiley & Sons (1991)
Goletsis, Y., Askounis, D.T., Psarras, J.: Multicriteria judgments for project ranking: An integrated methodology. Economic Financial Modelling 8(3), 127–148 (2001)
Jacquet-Lagrèze, E., Siskos, Y.: Preference disaggregation: 20 years of MCDA experience. European Journal of Operational Research 130(2), 233–245 (2001)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks 4, 1942–1948 (1995)
Mousseau, V., Slowinski, R., Zielniewicz, P.: ELECTRE TRI 2.0a: Methodological guide and user’s documentation. Université de Paris-Dauphine (1999)
Di Pillo, G., Grippo, L.: Exact penalty functions in constrained optimization. SIAM Journal on Control and Optimization 27(6), 1333–1360 (1989)
Roy, B.: ELECTRE III: Un algorithme de classements fondé sur une representation floue des préférences en présence de critères multiples. Cahiers du CERO 20(1), 3–24 (1978)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, pp. 69–73 (1998)
Thomaidis, N., Angelidis, T., Vassiliadis, V., Dounias, G.: Active portfolio management with cardinality constraints: An application of Particle Swarm Optimization. New Mathematics and Natural Computation 5(3), 535–555 (2009)
Zangwill, W.I.: Non-linear programming via penalty functions. Management Science 13(5), 344–358 (1967)
Zhang, W.J., Xie, X.F., Bi, D.C.: Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: Proceedings of the 2004 Congress on Evolutionary Computation IEEE, pp. 2307–2311 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Corazza, M., Funari, S., Gusso, R. (2014). A Methodological Proposal for an Evolutionary Approach to Parameter Inference in MURAME-Based Problems. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-04129-2_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
Online ISBN: 978-3-319-04129-2
eBook Packages: EngineeringEngineering (R0)