Abstract
In Multi-Criteria Decision Making (MCDM), decisions are based on several criteria that are usually conflicting and non-homogenously satisfied. Non-additive (fuzzy) measures along with the Choquet integral can model and aggregate the levels of satisfaction of these criteria by considering their relationships. However, in practice, it is difficult to identify such fuzzy measures. An automated process is necessary and can be done when sample data is available. In this article, we propose to use an adapted Bees algorithm to identify fuzzy measures from sample data. Our experimental results show that our Bees algorithm is faster and provides at least similar accuracy as or better than existing algorithms.
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