A many-objective problem suite (called multi-line distance minimisation problem, ML-DMP) whose Pareto optimal solutions in the2D decision space have similar distribution (in the sense of Euclidean geometry) to their images in the higher-dimensional objective space, thus allowing a direct observation of how the solution set are distributed in the high-dimensional space.
Geometric similarity on a tri-objective instance, where a set of uniformly-distributed points over the regular triangle in the 2D decision space corresponds to a set of uniformly-distributed objective vectors.
Results:
The solution sets of 15 EMO algorithms on a 4-objective ML-DMP instance (medium difficulty)
The solution sets of 15 EMO algorithms on a 10-objective ML-DMP instance (very hard)