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Novel supervisor-searcher cooperation algorithms for minimization problems with strong noise*The authors wish to express their sincere thanks to Mr K. Sirlantzis for his helpful comments and suggestions, and for his important contributions to the numerical implementation.

Author: Dai Yuhong   Lamb John D   Liu Wenbin  

Publisher: Taylor & Francis Ltd

ISSN: 1055-6788

Source: Optimization Methods and Software, Vol.18, Iss.3, 2003-06, pp. : 247-264

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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.