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

Liu, Wenbin, Dai, Yuhong, Lamb, John 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. (doi:10.1080/1055678031000119364) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8489)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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
DOI/Identification number: 10.1080/1055678031000119364
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Steve Liu
Date Deposited: 11 Sep 2008 13:05 UTC
Last Modified: 06 May 2020 03:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8489 (The current URI for this page, for reference purposes)
Liu, Wenbin: https://orcid.org/0000-0001-5966-6235
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