Hypervolume Approximation

Hypervolume approximation focuses on efficiently estimating the hypervolume indicator, a key metric for evaluating the performance of multi-objective optimization algorithms by quantifying the dominated region in objective space. Current research emphasizes developing accurate and computationally efficient approximation methods, employing techniques like deep neural networks (e.g., DeepSets, hypernetworks) and advanced quadrature methods (e.g., Gauss-Hermite) to overcome the computational challenges associated with high-dimensional problems. These advancements are crucial for improving the scalability and applicability of multi-objective optimization across diverse fields, including Bayesian optimization, evolutionary algorithms, and decision-making under uncertainty.