The field of optimization is witnessing significant developments, particularly in the areas of multi-objective optimization, Bayesian optimization, and reinforcement learning. Researchers are exploring innovative approaches to tackle complex problems, such as drug discovery, material design, and aviation decarbonization. A notable trend is the integration of human preferences and domain knowledge into optimization frameworks, enabling more efficient and effective search for optimal solutions. Another area of focus is the development of surrogate models and landscape learning techniques to improve the accuracy and scalability of optimization methods. Overall, these advancements have the potential to transform various fields by providing more powerful and efficient tools for decision-making and design optimization. Noteworthy papers include: Preference-Guided Diffusion for Multi-Objective Offline Optimization, which proposes a novel framework for generating Pareto-optimal designs using a classifier-based guidance mechanism. Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning, which introduces a method for automated feature learning during the meta-learning process, enhancing the performance of traditional black-box optimizers.