Answers are grounded in this paper and cite their sources.
This method reveals problem structures in multi-objective optimisation, suggesting new approaches to decision-making.
Understanding the structure of multi-objective optimisation problems (MOPs) is essential for analysing search difficulty and supporting informed decision-making. In single-objective optimisation, fitness landscapes offer a spatial view of a problem, but extending such visualisations to MOPs is challenging due to the vector-valued nature of objectives. In this work, we introduce Pareto landscape, a fitness landscape visualisation technique for multi-objective optimisation on the basis of the Pareto dominance relation. We illustrate the main characteristics of a Pareto landscape, relate it to the classical fitness landscape, and examine its behaviour across benchmark suites, constrained problems, multimodal problems and real-world cases. We also show how it captures problem landscape structures relevant to optimisation difficulty. A comparison with gradient field heatmaps, PLOT, cost landscape, and constrained cost landscape further demonstrates that Pareto landscape offers complementary insight by highlighting structural patterns not visible with existing visualisation methods. Overall, the results indicate that the Pareto landscape provides a consistent way to observe problem structure across different classes of multi-objective optimisation problems.
No takes yet. Share an insight, caveat, or question.
Cui et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: