Publication detail

A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING

SCHWARZ, J. JAROŠ, J.

Original Title

A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

This paper deals with the multiprocessor scheduling problem, which  belongs to the class of frequently solved decomposition tasks. The goals is to experimentally compare the performance of the recently proposed Mixed Bayesian Optimization Algorithm (MBOA) based on probabilistic model with  the newly derived  knowledge  based MBOA version (KMBOA) This algorithm includes  utilization of prior knowledge about the structure of a task graph to speed-up the  convergence  and the  solution quality. The performance of standard  genetic algorithm was also tested on the same benchmarks.

Keywords

optimization problems, multiprocessor scheduling problem, evolutionary algorithms, Bayesian optimization algorithm, problem knowledge.

Authors

SCHWARZ, J.; JAROŠ, J.

RIV year

2004

Released

28. 6. 2004

Publisher

Faculty of Mechanical Engineering BUT

Location

Brno

ISBN

80-214-2676-4

Book

Mendel Conference on Soft Computing

Pages from

83

Pages to

88

Pages count

6

BibTex

@inproceedings{BUT17336,
  author="Josef {Schwarz} and Jiří {Jaroš}",
  title="A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING",
  booktitle="Mendel Conference on Soft Computing",
  year="2004",
  pages="83--88",
  publisher="Faculty of Mechanical Engineering BUT",
  address="Brno",
  isbn="80-214-2676-4"
}