Many-Objective Problems: Challenges and Methods

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, M Open image in new windowOP 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 M Open image in new windowOPs are presented and categorized.

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