Multi Objective Bayesian Optimization

Multi-objective Bayesian optimization (MOBO) tackles the challenge of simultaneously optimizing multiple, often conflicting, objectives in computationally expensive scenarios. Current research focuses on improving the efficiency and robustness of MOBO algorithms, exploring diverse acquisition functions (e.g., those based on hypervolume improvement, entropy search, or Pareto front learning), and incorporating advanced model architectures like Gaussian processes and diffusion models to better handle high-dimensional spaces and complex Pareto fronts. These advancements are significantly impacting diverse fields, enabling more efficient design optimization in areas such as materials science, drug discovery, and machine learning hyperparameter tuning, leading to improved solutions and reduced computational costs.