Introduction
In this work we will look at connections between aggregation functions and optimization. There are two such connections: 1) aggregation functions are used to transform a multiobjective optimization problem into a single objective problem by aggregating several criteria into one, and 2) construction of aggregation functions often involves an optimization problem.
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
Alsina, C., Frank, M.J., Schweizer, B.: Associative Functions: Triangular Norms And Copulas. World Scientific, Singapore (2006)
Barral Souto, J.: El modo y otras medias, casos particulares de una misma expresion matematica. Boletin Matematico 11, 29–41 (1938)
Beliakov, G.: Shape preserving approximation using least squares splines. Approximation Theory and Applications 16, 80–98 (2000)
Beliakov, G.: Fitting triangular norms to empirical data. In: Klement, E.P., Mesiar, R. (eds.) Logical, Algebraic, Analytic, and Probabilistic Aspects of Triangular Norms, pp. 255–265. Elsevier, New York (2005)
Beliakov, G.: Monotonicity preserving approximation of multivariate scattered data. BIT 45, 653–677 (2005)
Beliakov, G.: Construction of aggregation operators for automated decision making via optimal interpolation and global optimization. J. of Industrial and Management Optimization 3, 193–208 (2007)
Beliakov, G.: Construction of aggregation functions from data using linear programming. Fuzzy Sets and Systems 160, 65–75 (2009)
Beliakov, G., Calvo, T., Lázaro, J.: Pointwise construction of Lipschitz aggregation operators with specific properties. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 15, 193–223 (2007)
Beliakov, G., Mesiar, R., Valášková, L.: Fitting generated aggregation operators to empirical data. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 12, 219–236 (2004)
Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners. Springer, Heidelberg (2007)
Ben-Tal, A., Charnes, A., Teboulle, M.: Entropic means. J. Math. Anal. Appl. 139, 537–551 (1989)
Bloomfield, P., Steiger, W.L.: Least Absolute Deviations. Theory, Applications and Algorithms. Birkhauser, Basel (1983)
Bregman, L.M.: The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Comput. Math. Phys. 7, 200–217 (1967)
Buchanan, B., Shortliffe, E.: Rule-based Expert Systems. In: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)
Bullen, P.S.: Handbook of Means and Their Inequalities. Kluwer, Dordrecht (2003)
Calvo, T., Kolesárová, A., KomornÃková, M., Mesiar, R.: Aggregation operators: properties, classes and construction methods. In: Calvo, T., Mayor, G., Mesiar, R. (eds.) Aggregation Operators. New Trends and Applications, pp. 3–104. Physica-Verlag, Heidelberg (2002)
Calvo, T., Mesiar, R.: Continuous generated associative aggregation operators. Fuzzy Sets and Systems 126, 191–197 (2002)
Calvo, T., Mesiar, R., Yager, R.: Quantitative weights and aggregation. IEEE Trans. on Fuzzy Systems 12, 62–69 (2004)
Carbonell, M., Mas, M., Mayor, G.: On a class of monotonic extended OWA operators. In: 6th IEEE International Conference on Fuzzy Systems, Barcelona, Spain, vol. III, pp. 1695–1700 (1997)
Dubois, D., Prade, H.: Fundamentals of Fuzzy Sets. Kluwer, Boston (2000)
Dubois, D., Prade, H.: On the use of aggregation operations in information fusion processes. Fuzzy Sets and Systems 142, 143–161 (2004)
Duda, R., Hart, P., Nilsson, N.: Subjective Bayesian methods for rule-based inference systems. In: Proc. Nat. Comput. Conf. (AFIPS), vol. 45, pp. 1075–1082 (1976)
Fuller, R., Majlender, P.: An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Sets and Systems 124, 53–57 (2001)
Fuller, R., Majlender, P.: On obtaining minimal variability OWA operator weights. Fuzzy Sets and Systems 136, 203–215 (2003)
Gini, C.: Le Medie. Unione Tipografico-Editorial Torinese, Milan (Russian translation, Srednie Velichiny, Statistica, Moscow, 1970) (1958)
Grabisch, M., Kojadinovic, I., Meyer, P.: A review of methods for capacity identification in Choquet integral based multi-attribute utility theory. Europ. J. Operations Research 186, 766–785 (2008)
Grabisch, M., Murofushi, T., Sugeno, M. (eds.): Fuzzy Measures and Integrals. Theory and Applications. Physica-Verlag, Heidelberg (2000)
Jackson, D.: Note on the median of a set of numbers. Bulletin of the Americam Math. Soc. 27, 160–164 (1921)
Kaymak, U., van Nauta Lemke, H.R.: Selecting an aggregation operator for fuzzy decision making. In: 3rd IEEE Intl. Conf. on Fuzzy Systems, vol. 2, pp. 1418–1422 (1994)
Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer, Dordrecht (2000)
KomornÃková, M.: Aggregation operators and additive generators. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 9, 205–215 (2001)
Liu, X.: The solution equivalence of minimax disparity and minimum variance problems for OWA operators. Int. J. Approx. Reasoning 45, 68–81 (2007)
Mesiar, R.: Fuzzy set approach to the utility, preference relations, and aggregation operators. Europ. J. Oper. Res. 176, 414–422 (2007)
Mesiar, R., Komornikova, M., Kolesarova, A., Calvo, T.: Aggregation functions: A revision. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Springer, Heidelberg (2008)
Mesiar, R., Å pirková, J., VavrÃková, L.: Weighted aggregation operators based on minimization. Inform. Sci. 178, 1133–1140 (2008)
Miettinen, K.: Nonlinear Multiobjective Optimization. Springer, New York (1998)
O’Hagan, M.O.: Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In: 22nd Annual IEEE Asilomar Conf. Signals, Systems, Computers, Pacific Grove, CA, pp. 681–689 (1988)
Pradera, A., Trillas, E., Calvo, T.: A general class of triangular norm-based aggregation operators: quasi-linear T-S operators. Intl. J. of Approximate Reasoning 30, 57–72 (2002)
Torra, V.: Learning weights for the quasi-weighted means. IEEE Trans. on Fuzzy Systems 10, 653–666 (2002)
Torra, Y., Narukawa, V.: Modeling Decisions. Information Fusion and Aggregation Operators. Springer, Heidelberg (2007)
Traub, J.F., Wozniakowski, H.: A General Theory of Optimal Algorithms. Academic Press, New York (1980)
Wang, Y.M., Parkan, C.: A minimax disparity approach for obtaining OWA operator weights. Inform. Sci. 175, 20–29 (2005)
Yager, R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. on Systems, Man and Cybernetics 18, 183–190 (1988)
Yager, R., Rybalov, A.: Uninorm aggregation operators. Fuzzy Sets and Systems 80, 111–120 (1996)
Yager, R., Rybalov, A.: Understanding the median as a fusion operator. Int. J. General Syst. 26, 239–263 (1997)
Zimmermann, H.-J., Zysno, P.: Latent connectives in human decision making. Fuzzy Sets and Systems 4, 37–51 (1980)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Beliakov, G. (2010). Optimization and Aggregation Functions. In: Lodwick, W.A., Kacprzyk, J. (eds) Fuzzy Optimization. Studies in Fuzziness and Soft Computing, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13935-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-13935-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13934-5
Online ISBN: 978-3-642-13935-2
eBook Packages: EngineeringEngineering (R0)