Novel supervisor-searcher cooperation algorithms for minimization problems with strong noise

Y. Dai, W. B. Liu, John Douglas Lamb

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This work continues the investigation in Ref. [1]: designing minimization algorithms in the framework of supervisor and searcher cooperation (SSC). It explores a wider range of possible supervisors and search engines to be used in the construction of SSC algorithms. Global convergence is established for algorithms with general supervisors and search engines in the absence of noise, and the convergence rate is studied. Both theoretical analysis and numerical results illustrate the appealing attributes of the proposed algorithms.

Original languageEnglish
Pages (from-to)247-264
Number of pages17
JournalOptimization Methods and Software
Volume18
Issue number3
DOIs
Publication statusPublished - Jun 2003

Keywords

  • supervisor searcher cooperation
  • global convergence
  • stochastic optimization
  • stochastic approximation

Cite this

Novel supervisor-searcher cooperation algorithms for minimization problems with strong noise. / Dai, Y.; Liu, W. B.; Lamb, John Douglas.

In: Optimization Methods and Software, Vol. 18, No. 3, 06.2003, p. 247-264.

Research output: Contribution to journalArticle

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