Cooperative Bayesian optimization algorithm

A novel approach to simultaneous multiple resources scheduling problem

X. Hao, X. Chen, H. W. Lin, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

During the past several years, there has been a significant amount of research conducted simultaneous multiple resources scheduling problem (SMRSP) Intelligence manufacturing based on meta-heuristics, such as genetic algorithms (GAs), simulated annealing (SA) particle swarm optimization(PSO), has become a common tool to find satisfactory solutions within reasonable computational times in real settings. However, there are few researches considering interdependent relation during the decision activities, moreover for complex and large problems, local constraints and objectives from each managerial entity cannot be effectively represented in a single model for complex and large problems. In this paper, we propose a novel cooperative Bayesian optimization algorithm (COBOA) undertaking divide-and-conquer strategy and co-evolutionary framework. Considerable experiments are conducted and the results confirmed that COBOA outperforms recent researches for the scheduling problem in FMS.

Original languageEnglish
Title of host publicationProceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011
Pages212-217
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011 - Shenzhen, Guangdong
Duration: 2011 Dec 162011 Dec 18

Other

Other2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011
CityShenzhen, Guangdong
Period11/12/1611/12/18

Fingerprint

Scheduling
Research
Simulated annealing
Intelligence
Particle swarm optimization (PSO)
Genetic algorithms
Experiments
Heuristics

Keywords

  • Bayesian network
  • coevolutionary algorithm
  • Estimization of distribution
  • multiple resources scheduling

ASJC Scopus subject areas

  • Biotechnology
  • Computational Theory and Mathematics
  • Computer Science Applications

Cite this

Hao, X., Chen, X., Lin, H. W., & Murata, T. (2011). Cooperative Bayesian optimization algorithm: A novel approach to simultaneous multiple resources scheduling problem. In Proceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011 (pp. 212-217). [6118606] https://doi.org/10.1109/IBICA.2011.57

Cooperative Bayesian optimization algorithm : A novel approach to simultaneous multiple resources scheduling problem. / Hao, X.; Chen, X.; Lin, H. W.; Murata, Tomohiro.

Proceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011. 2011. p. 212-217 6118606.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hao, X, Chen, X, Lin, HW & Murata, T 2011, Cooperative Bayesian optimization algorithm: A novel approach to simultaneous multiple resources scheduling problem. in Proceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011., 6118606, pp. 212-217, 2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011, Shenzhen, Guangdong, 11/12/16. https://doi.org/10.1109/IBICA.2011.57
Hao X, Chen X, Lin HW, Murata T. Cooperative Bayesian optimization algorithm: A novel approach to simultaneous multiple resources scheduling problem. In Proceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011. 2011. p. 212-217. 6118606 https://doi.org/10.1109/IBICA.2011.57
Hao, X. ; Chen, X. ; Lin, H. W. ; Murata, Tomohiro. / Cooperative Bayesian optimization algorithm : A novel approach to simultaneous multiple resources scheduling problem. Proceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011. 2011. pp. 212-217
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