Many-objective problems: Challenges and methods

Abstract

This chapter presents a short review of the state-of-the-art efforts for understanding and solving problems with a large number of objectives (usually known as many-objective optimization problems, MOP s). The first part of the chapter presents the current studies aimed at discovering the sources that make a multiobjective optimization problem (MOP) harder when more objectives are added, degrading in this way, the performance of a multiobjective evolutionary algorithm (MOEA). Next, some of the most relevant techniques designed to deal with MOPs are presented and categorized.