Constrained Multi Objective Evolutionary Algorithm

Constrained multi-objective evolutionary algorithms (CMOEAs) aim to find optimal solutions for problems with multiple conflicting objectives and limitations. Current research focuses on improving the efficiency and robustness of CMOEAs, particularly in handling complex constraints, such as those with unknown or stochastic components, by employing techniques like multi-task learning, adaptive tradeoff models, and novel indicator-based approaches. These advancements are crucial for tackling real-world optimization challenges across diverse fields, including engineering design, energy systems, and robotics, where finding feasible and optimal solutions under constraints is paramount.