Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25426
Title: A population-based incremental learning method for robust optimal solutions
Authors: Ho, SL 
Yang, S
Keywords: Inverse problem
Population-based incremental learning (PBIL)
Robust solution
Uncertainty
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on magnetics, 2010, v. 46, no. 8, 5512950, p. 3189-3192 How to cite?
Journal: IEEE transactions on magnetics 
Abstract: A population-based incremental learning (PBIL) method is proposed to search for both robust and global optimal solutions of an inverse problem in which there are inevitable tolerances on the decision variables. To reduce the computational costs of the proposed algorithm, a methodology for evaluating the expectancy measures and a philosophy for worst-case solutions are proposed. Moreover, a novel mechanism for selecting the performance metrics is introduced to enable the algorithm to find both global and robust optimal solutions in a single run. Two numerical examples are reported to validate the proposed algorithm.
URI: http://hdl.handle.net/10397/25426
ISSN: 0018-9464
EISSN: 1941-0069
DOI: 10.1109/TMAG.2010.2043650
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