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.
An Introduction to Many-Objective Evolutionary Optimization
Optimization Under Uncertainty with Applications to Aerospace Engineering ; Kapitel : 8 ; 269-305
2020-09-10
37 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Many-objective optimization , Evolutionary algorithms , Benchmarking , Surrogate model , High-dimension visualization Physics , Astronomy, Astrophysics and Cosmology , Aerospace Technology and Astronautics , Optimization , Mathematical and Computational Engineering , Computational Science and Engineering , Physics and Astronomy
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