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. | |
| 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: | Divisions > Kent Business School - Division > Kent Business School (do not use) |
| Depositing User: | Steve Liu |
| Date Deposited: | 11 Sep 2008 13:05 UTC |
| Last Modified: | 05 Nov 2024 09:40 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/8489 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Liu, Wenbin.
| Creator's ORCID: |
 https://orcid.org/0000-0001-5966-6235
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