Population Based

Population-based optimization algorithms are computational methods that iteratively improve solutions by maintaining and evolving a set of candidate solutions (a population). Current research focuses on enhancing these algorithms' efficiency and effectiveness, particularly through hybrid approaches combining them with machine learning, quantum computing, and active inference to improve exploration and exploitation of the search space. These advancements are impacting diverse fields, from engineering design optimization and supply chain management to reinforcement learning and solving complex combinatorial problems, by enabling faster and more robust solutions to computationally expensive problems. The development of new algorithms and improved strategies for handling mixed-integer variables and high-dimensional spaces remains a key area of focus.