Novel Supervisor-Searcher Cooperation Algorithms For Minimization Problems With Strong Noise

Liu, W.B. and Dai, Y. and Lamb, J.D. (2003) Novel Supervisor-Searcher Cooperation Algorithms For Minimization Problems With Strong Noise. Optimization Methods and Software, 18 (3). pp. 246-264. ISSN 1055-6788 . (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1080/1055678031000119364

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

Item Type: Article
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Steve Wenbin Liu
Date Deposited: 11 Sep 2008 13:05
Last Modified: 14 Jan 2010 14:31
Resource URI: http://kar.kent.ac.uk/id/eprint/8489 (The current URI for this page, for reference purposes)
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