Decomposition Based

Decomposition-based methods are a dominant approach in multi-objective optimization, aiming to solve complex problems by breaking them down into simpler, single-objective subproblems. Current research focuses on improving algorithms like MOEA/D, refining decomposition strategies (e.g., integrating normal and penalty-based boundary intersection methods), and developing more efficient reference point selection and adaptation techniques, particularly for high-dimensional problems. These advancements enhance the ability to find diverse and high-quality Pareto optimal solutions, impacting fields requiring efficient exploration of trade-offs between multiple objectives, such as engineering design and resource allocation.