Multiple Objective

Multiple objective optimization tackles the challenge of finding optimal solutions when multiple, often conflicting, goals must be considered simultaneously. Current research focuses on developing algorithms and frameworks that efficiently explore the trade-off space between objectives, including advancements in Bayesian optimization, reinforcement learning (with methods like multi-objective gradient aggregation and decomposition), and evolutionary algorithms. These methods are applied across diverse fields, from robotics and AI alignment to resource allocation and engineering design, improving decision-making in complex systems where single-objective approaches are insufficient. The ultimate aim is to provide robust and efficient tools for finding Pareto-optimal solutions that represent the best possible compromises across all objectives.