Unconstrained real valued optimization based on stochastic differential equations

Vegh, Viktor and Tieng, Quang (2011) Unconstrained real valued optimization based on stochastic differential equations. International Journal of Innovative Computing, Information and Control, 7 11: 6235-6246.

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Author Vegh, Viktor
Tieng, Quang
Title Unconstrained real valued optimization based on stochastic differential equations
Journal name International Journal of Innovative Computing, Information and Control
ISSN 1349-4198
Publication date 2011-11
Sub-type Article (original research)
Volume 7
Issue 11
Start page 6235
End page 6246
Total pages 12
Place of publication Kumamoto, Japan
Publisher I C I C International
Collection year 2012
Language eng
Abstract A new approach of finding optimal variables to continuous, real valued functions is outlined. The technique is simple to implement, and does not require multiple instances of the variables or knowledge of the cost function derivative. The variable update is governed by the numerical solution to a specific stochastic differential equation. A number of parameters can be changed to alter the manner in which the variables are updated. Different parameter choices lead to changes in the rate and manner of convergence to the optimal solution. Test functions are used to establish method performance. Results show that the method is competitive with established approaches.
Keyword Evolutionary computing
Globally convergent
Optimization
Stochastic differential equation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
Centre for Advanced Imaging Publications
 
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Created: Fri, 21 Oct 2011, 16:49:48 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging