Multi‐Objective Evolutionary Algorithms and Evolutionary Large‐Scale Optimization

This chapter focuses on problems of large scale multi‐objective optimization problems (LSMOPs) and large scale many‐objective optimization problems. It refers to research adopting evolutionary algorithms (EAs) to solve large‐scale optimization problems as evolutionary large‐scale optimization. The chapter introduces multi‐objective EAs and overviews evolutionary large‐scale optimization. On the one hand, to tailor EAs for solving LSMOPs, the related works are highly related to multi‐objective evolutionary algorithms (MOEAs). While conventional MOEAs were developed to solve problems with relatively small‐scale decision variables, the core mechanisms behind them have founded further development toward evolutionary large‐scale multi‐objective optimization. On the other hand, when dealing with large‐scale decision variables, ideas are also borrowed from the single objective optimization literature. Particularly, the divide‐and‐conquer strategy deals with large‐scale decision variables.