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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Solving Transit Network Design Problem Using Many-Objective Evolutionary Approach

    Nayeem, Muhammad Ali / Islam, Md. Monirul / Yao, Xin | IEEE | 2019




    Multi-Objective Hybrid Evolutionary Optimization with Automatic Switching

    Moral, R. / Sahoo, D. / Dulikravich, G. et al. | British Library Conference Proceedings | 2006


    Evolutionary multi-objective portfolio optimization in practical context

    Chiam, S. C. / Tan, K. C. / Mamum, A. | British Library Online Contents | 2008