Abstract Complex many-objective optimization problems (MaOPs) generate multiple challenges for obtaining convergence alongside diversity within extensive multi-dimensional solution areas. Optimization approaches currently face limitations when trying to balance exploration and exploitation especially when resources become limited. MaOCO represents the Many-Objective Cheetah Optimization Algorithm which draws its concepts from the hunting behavior of cheetahs. MaOCO includes adaptive search functions that use attac…