Evolutionary Multi Objective Optimization

Evolutionary Multi-Objective Optimization (EMO) tackles problems with multiple, often conflicting, objectives by using evolutionary algorithms to find a set of optimal trade-offs, known as the Pareto front. Current research emphasizes improving the efficiency and scalability of EMO algorithms, focusing on advancements in techniques like decomposition-based methods (e.g., MOEA/D), novel selection mechanisms (e.g., sliding window approaches), and the integration of machine learning (e.g., diffusion models). These advancements are crucial for addressing complex real-world problems across diverse fields, from engineering design and resource allocation to financial modeling and autonomous systems, where finding optimal solutions considering multiple competing factors is essential.