A Methodological Proposal for an Evolutionary Approach to Parameter Inference in MURAME-Based Problems

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)

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.

Keywords

Preference disaggregation MURAME Particle Swarm Optimization 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marco Corazza
    • 1
    • 3
  • Stefania Funari
    • 2
  • Riccardo Gusso
    • 1
  1. 1.Department of EconomicsCa’ Foscari University of VeniceVeniceItaly
  2. 2.Department of ManagementCa’ Foscari University of VeniceVeniceItaly
  3. 3.Advanced School of Economics of VeniceCa’ Foscari University of VeniceVeniceItaly

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