Optimization Objective

Optimization objectives define the goals of an optimization process, crucial for diverse applications from machine learning to manufacturing. Current research emphasizes dynamic and adaptive objective selection, particularly in multi-objective scenarios where conflicting goals (e.g., accuracy and fairness in machine learning, makespan and tardiness in manufacturing) must be balanced, often using evolutionary algorithms or reinforcement learning. These advancements improve the efficiency and robustness of optimization, leading to better solutions in various fields and informing the development of more sophisticated algorithms and model architectures.