Evolutionary Multi Objective Optimization Algorithm

Evolutionary multi-objective optimization (EMO) algorithms aim to efficiently find a set of optimal solutions that balance multiple, often conflicting, objectives. Current research focuses on improving the runtime performance of algorithms like NSGA-II and SMS-EMOA, particularly for many-objective problems and incorporating structural constraints on the solution set, as well as developing effective normalization techniques and efficient subset selection methods for handling large solution archives. These advancements are crucial for tackling complex real-world problems across diverse fields, such as neural architecture search and engineering design, where multiple performance criteria must be considered simultaneously.