Publication detail

Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice

SCHWARZ, J., OČENÁŠEK, J.

Original Title

Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice

Type

journal article - other

Language

English

Original Abstract

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.

Keywords

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

Authors

SCHWARZ, J., OČENÁŠEK, J.

RIV year

2001

Released

9. 7. 2001

ISBN

1210-0552

Periodical

NEURAL NETWORK WORLD

Year of study

11

Number

5

State

Czech Republic

Pages from

423

Pages to

441

Pages count

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"
}