A new method for global optimization
newmethodforglob00hart.pdf (753.2Kb)Abstract
The basic descent algorithms for minimizing nonlinear objective functions will generally find a local minimum. For problems with multimodal objective functions, it is desirable to extend the search in an attempt to find a global minimum. Several versions of a new method for doing this are presented. Computational tests are performed to compare these methods with existing methods. (Author) The basic descent algorithms for minimizing nonlinear
objective functions will generally find a local minimum.
For problems with multimodal objective functions, it is
desirable to extend the search in an attempt to find a
global minimum. Several versions of a new method for doing
this are presented. Computational tests are performed to
compare these methods with each other and with existing methods.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.NPS Report Number
NPS55HH73041ARelated items
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