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Detail publikace
HOLEŠOVSKÝ, J.
Originální název
Distribution Endpoint Estimation Assessment for the Use in Metaheuristic Optimization Procedure
Typ
článek v časopise ve Scopus, Jsc
Jazyk
angličtina
Originální abstrakt
Metaheuristic algorithms are often applied to numerous optimization problems, involving large-scale and mixed-integer instances, specifically. In this contribution we discuss some refinements from the extreme value theory to the lately proposed modification of partition-based random search. The partition-based approach performs iterative random sampling at given feasible subspaces in order to exclude the less favourable regions. The quality of particular regions is evaluated according to the promising index of a region. From statistical perspective, determining the promising index is equivalent to the endpoint estimation of a probability distribution induced by the objective function at the sampling subspace. In the following paper, we give a short review of the recent endpoint estimators derived on the basis of extreme value theory, and compare them by simulations. We discuss also the difficulties in their application and suitability of the estimators for various optimization instances.
Klíčová slova
metaheuristic optimization; endpoind estimation; extreme value; random search; bootstrap; order statistics.
Autoři
Vydáno
26. 6. 2018
Nakladatel
Brno University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science
Místo
Brno, Czech Republic
ISSN
1803-3814
Periodikum
Mendel Journal series
Ročník
24
Číslo
1
Stát
Česká republika
Strany od
93
Strany do
100
Strany počet
8
URL
https://www.scopus.com/record/display.uri?eid=2-s2.0-85072045104&origin=inward&txGid=401c4364415767d13681ff39fe8d0059
BibTex
@article{BUT148580, author="Jan {Holešovský}", title="Distribution Endpoint Estimation Assessment for the Use in Metaheuristic Optimization Procedure", journal="Mendel Journal series", year="2018", volume="24", number="1", pages="93--100", doi="10.13164/mendel.2018.1.093", issn="1803-3814", url="https://www.scopus.com/record/display.uri?eid=2-s2.0-85072045104&origin=inward&txGid=401c4364415767d13681ff39fe8d0059" }