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Visualization-aided multi-criteria decision-making using interpretable self-organizing maps
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AI
DOI：10.1016/j.ejor.2023.01.062.png)
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Abstract
En 中文
In multi-criterion optimization, decision-makers (DMs) are not often interested in the complete Pareto-optimal front. Instead, they have preferences favoring specific parts of the front. Multi-criterion decision -making (MCDM) literature provides a plethora of approaches for introducing DM's preference information in an interactive manner to solve multi-criterion optimization problems. Interactions with DMs can be aided with a user-friendly visualization method or by using special data analysis procedures. An earlier study has indicated the use of self-organizing maps (SOM) as a tool for analyzing Pareto-optimal solu-tions. In this paper, we demonstrate how a specific MCDM method - NIMBUS - can be executed with the interpretable SOM (iSOM) approach iteratively to arrive at one or more preferred solutions. A visual illustration of the entire high-dimensional search space into multiple reduced two-dimensional spaces allows DMs to have a better understanding of the interactions of the objectives and constraints indepen-dently, and execute the NIMBUS decision-making procedure with a more wholistic approach. The paper demonstrates the proposed method on a number of multi-and many-objective numerical and engineer-ing problems. The approach is now ready to be integrated with other popular MCDM methods.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Multiple criteria analysis
Evolutionary multi -criterion optimization
Multi -criteria decision making
NIMBUS
Self -organizing maps
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W