Single Objective

Single-objective optimization focuses on finding the single best solution to a problem by maximizing or minimizing a single objective function. Current research emphasizes developing robust and efficient algorithms, such as enhanced versions of differential evolution and memetic algorithms, often incorporating multiple search operators or restart mechanisms to escape local optima. These advancements are crucial for tackling complex real-world problems across diverse fields, from engineering design to machine learning, where finding optimal solutions is computationally expensive or involves noisy data. The development and rigorous analysis of these algorithms are driving progress in optimization theory and practice.