---
res:
bibo_abstract:
- "We present a flexible trust region descend algorithm for unconstrained and\r\nconvexly
constrained multiobjective optimization problems. It is targeted at\r\nheterogeneous
and expensive problems, i.e., problems that have at least one\r\nobjective function
that is computationally expensive. The method is\r\nderivative-free in the sense
that neither need derivative information be\r\navailable for the expensive objectives
nor are gradients approximated using\r\nrepeated function evaluations as is the
case in finite-difference methods.\r\nInstead, a multiobjective trust region approach
is used that works similarly to\r\nits well-known scalar pendants. Local surrogate
models constructed from\r\nevaluation data of the true objective functions are
employed to compute\r\npossible descent directions. In contrast to existing multiobjective
trust\r\nregion algorithms, these surrogates are not polynomial but carefully\r\nconstructed
radial basis function networks. This has the important advantage\r\nthat the number
of data points scales linearly with the parameter space\r\ndimension. The local
models qualify as fully linear and the corresponding\r\ngeneral scalar framework
is adapted for problems with multiple objectives.\r\nConvergence to Pareto critical
points is proven and numerical examples\r\nillustrate our findings.@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Manuel Bastian
foaf_name: Berkemeier, Manuel Bastian
foaf_surname: Berkemeier
foaf_workInfoHomepage: http://www.librecat.org/personId=51701
- foaf_Person:
foaf_givenName: Sebastian
foaf_name: Peitz, Sebastian
foaf_surname: Peitz
foaf_workInfoHomepage: http://www.librecat.org/personId=47427
orcid: 0000-0002-3389-793X
bibo_doi: 10.3390/mca26020031
bibo_issue: '2'
bibo_volume: 26
dct_date: 2021^xs_gYear
dct_isPartOf:
- http://id.crossref.org/issn/2297-8747
dct_language: eng
dct_title: Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis
Function Surrogate Models@
...