The recent developments in the research area of optimization and decision-making have shown a significant shift towards non-myopic and multi-objective approaches. Researchers are increasingly focusing on strategies that not only optimize immediate outcomes but also consider long-term implications, exemplified by the introduction of non-myopic acquisition functions in deterministic optimization frameworks. These methods, which leverage dynamic programming and lookahead strategies, represent a substantial advancement over traditional myopic approaches, particularly in high-dimensional spaces where computational efficiency is paramount. Additionally, the integration of transfer learning and adaptive mechanisms within optimization processes, such as those employing Monte Carlo Tree Search, has demonstrated superior performance in identifying promising search spaces and leveraging prior knowledge from similar tasks. In the realm of multi-objective optimization, the development of non-myopic Bayesian optimization techniques has opened new avenues for tackling complex, real-world problems by considering multiple objectives simultaneously. These advancements are complemented by innovative software tools and frameworks that facilitate the integration and development of novel algorithms, enhancing both research and practical applications. Notably, the use of genetic programming for generating intelligent search strategies in Branch and Bound algorithms has shown promise in achieving a balance between computational efficiency and decision-making accuracy. Overall, the field is progressing towards more sophisticated, adaptive, and computationally efficient optimization strategies that are better equipped to handle the complexities of modern problems.