Detail publikace

Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice

SCHWARZ, J. OČENÁŠEK, J.

Originální název

Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the multiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow to search effectively on the promising areas of the combinatorial search space are discussed. The main attention is focused on the incorporation of Pareto optimality concept into classical structure of the BOA algorithm. We have modified the standard algorithm BOA for one criterion optimization utilizing the known niching techniques to find the Pareto optimal set. The experiments are focused on tree classes of the combinatorial problems: artificial problem with known Pareto set, multiple 0/1 knapsack problem and the bisectioning of hypergraphs as well.

Klíčová slova

Multiobjective optimization, Pareto and non Pareto algorithms, evolutionary algorithms, probabilistic model, estimation distribution algorithms, Bayesian optimization algorithm, niching techniques

Autoři

SCHWARZ, J.; OČENÁŠEK, J.

Rok RIV

2001

Vydáno

9. 7. 2001

ISSN

1210-0552

Periodikum

NEURAL NETWORK WORLD

Ročník

11

Číslo

5

Stát

Česká republika

Strany od

423

Strany do

441

Strany počet

19

URL

http://www.fit.vutbr.cz/~schwarz/PDFCLANKY/nnw01.ps

BibTex

@article{BUT40359,
  author="Josef {Schwarz} and Jiří {Očenášek}",
  title="Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice",
  journal="NEURAL NETWORK WORLD",
  year="2001",
  volume="11",
  number="5",
  pages="423--441",
  issn="1210-0552",
  url="http://www.fit.vutbr.cz/~schwarz/PDFCLANKY/nnw01.ps"
}