Detail publikačního výsledku

Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems

SCHWARZ, J.; OČENÁŠEK, J.; JAROŠ, J.

Originální název

Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems

Anglický název

Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

This paper deals with the usage of Bayesian optimization algorithm (BOA) and its advanced variants for solving complex NP-complete combinatorial optimization problems. We focus on the hypergraph-partitioning problem and multiprocessor scheduling problem, which belong to the class of frequently solved decomposition tasks. One of the goals is to use these problems to experimentally compare the performance of the recently proposed Mixed Bayesian Optimization Algorithm (MBOA) with the performance of several other evolutionary algorithms. BOA algorithms are based on the estimation and sampling of probabilistic model unlike classical genetic algorithms. We also propose the utilization of prior knowledge about the structure of a task graph to increase the convergence speed and the quality of solutions. The performance of KMBOA algorithm on the multiprocessor scheduling problem is empirically investigated and confirmed.

Anglický abstrakt

This paper deals with the usage of Bayesian optimization algorithm (BOA) and its advanced variants for solving complex NP-complete combinatorial optimization problems. We focus on the hypergraph-partitioning problem and multiprocessor scheduling problem, which belong to the class of frequently solved decomposition tasks. One of the goals is to use these problems to experimentally compare the performance of the recently proposed Mixed Bayesian Optimization Algorithm (MBOA) with the performance of several other evolutionary algorithms. BOA algorithms are based on the estimation and sampling of probabilistic model unlike classical genetic algorithms. We also propose the utilization of prior knowledge about the structure of a task graph to increase the convergence speed and the quality of solutions. The performance of KMBOA algorithm on the multiprocessor scheduling problem is empirically investigated and confirmed.

Klíčová slova

Bayesian optimization algorithm, hypergraph-partitioning problem, multiprocessor scheduling problem, specific problem knowledge

Klíčová slova v angličtině

Bayesian optimization algorithm, hypergraph-partitioning problem, multiprocessor scheduling problem, specific problem knowledge

Autoři

SCHWARZ, J.; OČENÁŠEK, J.; JAROŠ, J.

Vydáno

28.06.2004

Nakladatel

IEEE Computer Society

Místo

Los Alamitos

ISBN

0-7695-2125-8

Kniha

Proceedings of ECBS 2004

Strany od

102

Strany do

111

Strany počet

10

BibTex

@inproceedings{BUT17153,
  author="Josef {Schwarz} and Jiří {Očenášek} and Jiří {Jaroš}",
  title="Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems",
  booktitle="Proceedings of ECBS 2004",
  year="2004",
  pages="102--111",
  publisher="IEEE Computer Society",
  address="Los Alamitos",
  isbn="0-7695-2125-8"
}