Multi Objective Evolutionary Algorithm

Multi-objective evolutionary algorithms (MOEAs) are computational methods designed to find optimal solutions for problems with multiple, often conflicting, objectives. Current research emphasizes improving MOEA efficiency and interpretability, focusing on adaptive mechanisms, knowledge integration (e.g., using large language models or heuristic rules), and visual analytics tools to understand population dynamics. These advancements are significant because MOEAs are increasingly applied to complex real-world problems across diverse fields, including engineering design, scheduling, and drug discovery, where efficient and robust optimization is crucial.