Constrained Multi Objective Optimization Problem

Constrained multi-objective optimization problems (CMOPs) involve finding the best compromise solutions across multiple conflicting objectives, while satisfying a set of constraints. Current research focuses on improving the efficiency and effectiveness of algorithms, including evolutionary algorithms enhanced by large language models and first-order methods, to navigate complex search spaces and handle scenarios with unknown or difficult-to-evaluate constraints. These advancements are crucial for tackling real-world problems in diverse fields like portfolio optimization and engineering design, where multiple objectives and constraints are inherent. A deeper understanding of CMOP characteristics, through instance space analysis, is also driving the development of more robust and reliable optimization methods.