Many Objective

Many-objective optimization tackles the challenge of finding optimal solutions when multiple, often conflicting, objectives must be considered simultaneously. Current research focuses on developing and analyzing the runtime performance of evolutionary algorithms like NSGA-III and SEMO, improving their efficiency and scalability for high-dimensional problems, and exploring novel approaches such as optimal transport-based methods to handle a large number of objectives exceeding the number of solutions. These advancements are crucial for addressing real-world problems in diverse fields like machine learning and engineering design, where finding balanced trade-offs across numerous objectives is essential for optimal system performance.