This chapter describes the differences between single-objective, multi-objective, and many-objective optimization problems. In multi- and many-objective optimization, often the objectives are conflicting; hence there is no single best point, and a trade-off between the objectives must be considered. Many-objective optimization problems can be more difficult than multi-objective problems mainly because of the curse of dimensionality and because it is also difficult to visualize the trade-off between the objectives. To solve many-objective optimization problems, some algorithms are designed with the challenges in consideration. These algorithms are also described in this chapter, including surrogate-assisted algorithms. Furthermore, several benchmark problems to test and compare the algorithms are discussed.


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