RT Journal Article
T1 A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm
A1 Ruiz-Mora, Ana Belén
A1 Saborido Infantes, Rubén
A1 Luque-Gallego, Mariano
K1 Algoritmos
K1 Programación evolutiva (Informática)
K1 Optimización matemática
AB When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front. In this paper, we suggest a preference-based EMO algorithm called weighting achievement scalarizing function genetic algorithm (WASF-GA), which considers the preferences of the decision maker (DM) expressed by means of a reference point. The main purpose of WASF-GA is to approximate the region of interest of the Pareto optimal front determined by the reference point, which contains the Pareto optimal objective vectors that obey the preferences expressed by the DM in the best possible way. The proposed approach is based on the use of an achievement scalarizing function (ASF) and on the classification of the individuals into several fronts. At each generation of WASF-GA, this classification is done according to the values that each solution takes on the ASF for the reference point and using different weight vectors. These vectors of weights are selected so that the vectors formed by their inverse components constitute a well-distributed representation of the weight vectors space. The efficiency and usefulness of WASF-GA is shown in several test problems in comparison to other preference-based EMO algorithms. Regarding a metric based on the hypervolume, we can say that WASF-GA has outperformed the other algorithms considered in most of the problems.
PB Springer
YR 2014
FD 2014
LK https://hdl.handle.net/10630/34054
UL https://hdl.handle.net/10630/34054
LA eng
NO Ruiz, A.B., Saborido, R. & Luque, M. A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm. J Glob Optim 62, 101–129 (2015). https://doi.org/10.1007/s10898-014-0214-y
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 12 abr 2026