A multiobjective evolutionary algorithm for deriving a final ranking from a medium-sized fuzzy outranking relation

Juan Carlos Leyva-López, Jesús Jaime Solano-Noriega and Diego Alonso Gastélum-Chavira

International Journal of Entrepreneurship and Innovation Management, 2021, vol. 25, issue 2/3, 184-210

Abstract: The exploitation stage of a fuzzy outranking relation of the outranking methods aims to deal with the intransitivities in such a relation to consolidate and synthesise the decision maker's preferences between pairs of actions to obtain a ranking that is representative of his/her preferences over a given set of actions. In this paper, we propose this exploitation phase as a multiobjective optimisation problem and use a modified version of the multiobjective genetic algorithm (we refer to it as RP²-MOGA+H) to exploit a medium-sized fuzzy outranking relation to determining a partial pre-order of alternatives. To measure the performance of RP²-MOGA+H, we present an empirical study over a set of simulated ranking problems, which shows that RP²-MOGA+H can effectively exploit a medium-sized fuzzy outranking relation. Moreover, the RP²-MOGA+H outperforms other ranking procedures based on multiobjective evolutionary algorithms in the conducted experiments.

Keywords: multicriteria decision analysis; MCDA; fuzzy outranking relations; multiobjective evolutionary algorithms; MOEA; ranking procedures. (search for similar items in EconPapers)
Date: 2021
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