Objective Optimization

Objective optimization, particularly in its multi-objective form, seeks to find optimal solutions considering multiple, often conflicting, criteria. Current research focuses on improving the efficiency of algorithms, such as memetic algorithms incorporating reinforcement learning and diffusion models, to handle complex constraints and large-scale problems across diverse applications, including scheduling, exoskeleton design, and machine learning. These advancements are significant for improving the performance and scalability of optimization techniques in various fields, leading to better solutions in areas like resource allocation, engineering design, and fairness-aware machine learning.