Visualization techniques in design space exploration with high dimensional data are helpful in enhancing decision-making in the context of multiple objective optimizations. Visualization of Pareto solutions obtained is crucial to understand the trade-off between the objectives as it enables intuitive decision-making. However, such a task is not trivial beyond three dimensions. In this work, we propose using an interpretable self-organizing map (iSOM), to visualize Pareto solutions for MOO problems involving n objectives (n > 3). iSOM enables simplified component plane plots that allow visual inspection of the Pareto fronts and also allow identifying clusters in the Pareto front and the corresponding design variables. The proposed approach is successfully demonstrated on 3 analytical examples.

Keywords: multi-objective optimization · Pareto solution set · Pareto front · visualization · self-organizing maps · iSOM.