Manuel Colmenar, Jose ManuelHerrán, AlbertoMartín-Santamaría, Raúl2024-12-212024-12-212023-11-17978-3-031-38309-0978-3-031-38310-6https://hdl.handle.net/10115/45958Diversity problems are usually studied from a single-objective point of view. However, two or more diversity functions could present opposite or divergent behavior, which requires a multi-objective point of view. To illustrate this kind of problems, this chapter presents the study of the bi-objective diversity problem (BODP), which considers the MaxSum and the MaxMin as objective functions to simultaneously maximize. Six different multi-objective algorithms have been described, analyzing their results on six performance metrics using a subset of instances from the MDPLIB 2.0 library.Diversity problems are usually studied from a single-objective point of view. However, two or more diversity functions could present opposite or divergent behavior, which requires a multi-objective point of view. To illustrate this kind of problems, this chapter presents the study of the bi-objective diversity problem (BODP), which considers the MaxSum and the MaxMin as objective functions to simultaneously maximize. Six different multi-objective algorithms have been described, analyzing their results on six performance metrics using a subset of instances from the MDPLIB 2.0 library.en-USAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Book chapter10.1007/978-3-031-38310-6_14info:eu-repo/semantics/closedAccess