Multi Objective

Multi-objective optimization tackles problems with multiple, often conflicting, objectives, aiming to find optimal trade-offs rather than a single best solution. Current research focuses on developing efficient algorithms, such as evolutionary algorithms (e.g., NSGA-II, MOEA/D), multi-objective reinforcement learning techniques, and novel architectures like transformer networks, to address this challenge across diverse applications. These advancements are improving the design of neural networks, recommender systems, and robotic control systems, among other areas, by enabling the simultaneous optimization of various performance metrics and constraints. The resulting Pareto-optimal solutions offer valuable insights and flexibility for decision-making in complex systems.