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Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice

SCHWARZ Josef a OČENÁŠEK Jiří. Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice. NEURAL NETWORK WORLD, roč. 11, č. 5, 2001, s. 423-441. ISSN 1210-0552.
Název česky
Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice
Typ
článek v časopise
Jazyk
angličtina
Autoři
Schwarz Josef, Ing., CSc. (UIVT FEI VUT)
Očenášek Jiří, Ing. (UIVT FEI VUT)
URL
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.

Rok
2001
Strany
423-441
Časopis
NEURAL NETWORK WORLD, roč. 11, č. 5, ISSN 1210-0552
BibTeX 
@ARTICLE{FITPUB6762,
   author = "Josef Schwarz and Ji\v{r}\'{i} O\v{c}en\'{a}\v{s}ek",
   title = "Multiobjective Bayesian Optimization Algorithm for Combinatorial Problems: Theory and Practice",
   pages = "423--441",
   journal = "NEURAL NETWORK WORLD",
   volume = 11,
   number = 5,
   year = 2001,
   ISSN = "1210-0552",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/6762"
}
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