Table 6. Analysis of dimensional complexity in MOO.

Function assignments are based on common usage patterns, not strict dimensional constraints.

Dimension	Problem characteristics	Representative benchmark functions
Low (2–3)	Simple geometric structure of the Pareto front (convex, concave, or disconnected); easy to visualize and analyze	ZDT Series (Zitzler, Deb & Thiele, 2000), Kursawe Function (Deb et al., 2002a)
Medium (4–10)	Emergence of the curse of dimensionality; sparse distribution of solutions; reduced search efficiency	DTLZ Series (Deb et al., 2002b), WFG Toolkit (Huband et al., 2006)
High (>10)	Severe objective redundancy; loss of dominance pressure; need for dimensionality reduction or adaptive reference-based strategies	MaF Series (Zhang, Liu & Yao, 2023), MAOP, LSMOP (Kalita et al., 2024)