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A coevolutionary algorithm for exploiting a large fuzzy outranking relation
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AI
DOI：10.1016/j.ejor.2024.12.012.png)
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Abstract
En 中文
The outranking approach in Multiple Criteria Decision Analysis (MCDA) uses ranking procedures to exploit a fuzzy outranking relation, which captures the decision maker's notion of a ranking. However, as decision problems become more complex and computer performance improves, new ranking procedures are needed to rank complex data sets that decision-makers may not interpret. This paper discusses recent efforts and potential directions for developing ranking procedures that use multiobjective evolutionary algorithms (MOEAs) to exploit a fuzzy outranking relation. After that, based on the cooperative coevolutionary algorithms (CCEA) approach, we suggest some fundamental modifications to extend the RP2-NSGA-II+H algorithm that improve the scalability of this MOEA to exploit large-sized fuzzy outranking relations. Empirical results indicate that adjustments improve the RP2-NSGA-II+H algorithm for the addressed problem. The proposed ranking procedure outperforms RP2- NSGA-II+H in terms of ranking error rates based on the experiments conducted. Our experimental results also demonstrate that the proposed approach can be scaled for instances of the ranking problem of up to one thousand alternatives.
Keywords:
Multiple criteria decision analysis
Fuzzy outranking relations
Multiobjective evolutionary algorithms
Cooperative coevolutionary algorithms
Ranking procedures
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W