Multi Objective Optimization

Multi-objective optimization (MOO) tackles the challenge of simultaneously optimizing multiple, often conflicting, objectives, aiming to find a set of optimal trade-off solutions (the Pareto front). Current research focuses on improving the efficiency and scalability of MOO algorithms, particularly for large-scale problems, with a growing emphasis on gradient-based methods and the integration of machine learning techniques like deep reinforcement learning and large language models to enhance search capabilities and automate algorithm design. The ability to effectively handle multiple objectives has significant implications across diverse fields, including robotics, machine learning model design, autonomous systems, and ecological monitoring, enabling more robust and adaptable solutions to complex real-world problems.