Multiobjective Optimization

Multiobjective optimization tackles problems with multiple, often conflicting, objectives, aiming to find a set of optimal solutions representing trade-offs between them, known as the Pareto front. Current research emphasizes developing efficient algorithms, including gradient-based methods, hybrid approaches combining deep reinforcement learning with evolutionary algorithms like NSGA-II, and novel techniques like those using tensorization for GPU acceleration and machine learning for search space reduction. This field is crucial for diverse applications, from resource allocation and materials design to neural architecture search and robotic control, offering improved solutions where single-objective approaches fall short.