Visualization-aided multi-criteria decision-making using interpretable self-organizing maps

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.

Automated summary

In multi-criterion optimization, decision-makers often have preferences favoring specific parts of the Pareto-optimal front. The literature on multi-criterion decision-making provides various approaches for incorporating decision-makers' preferences. This paper demonstrates how the NIMBUS method can be executed with the interpretable self-organizing map (iSOM) approach to arrive at preferred solutions iteratively.