A metaheuristic optimization-based indirect elicitation of preference parameters for solving many-objective problems

DOI

10.2991/ijcis.2017.10.1.5How to use a DOI?

Keywords

metaheuristic; decision aid; parameter inference; indirect approach; preference analysis disaggregation

Abstract

A priori incorporation of the decision maker’s preferences is a crucial issue in many-objective evolutionary optimization. Some approaches characterize the best compromise solution of this problem through fuzzy outranking relations; however, they require the elicitation of a large number of parameters (weights and different thresholds). This paper proposes a novel metaheuristic-based optimization method to infer the model’s parameters of a fuzzy relational system of preferences, based on a small number of judgments given by the decision maker. The results show a satisfactory rate of error when predicting new outcomes with the parameter values obtained by using small size reference sets.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Laura Cruz-Reyes
AU  - Eduardo Fernandez
AU  - Nelson Rangel-Valdez
PY  - 2017
DA  - 2017/01/01
TI  - A metaheuristic optimization-based indirect elicitation of preference parameters for solving many-objective problems
JO  - International Journal of Computational Intelligence Systems
SP  - 56
EP  - 77
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.5
DO  - 10.2991/ijcis.2017.10.1.5
ID  - Cruz-Reyes2017
ER  -