Stochastic optimization algorithm with probability vector

Authors:
Jan Pohl;Václav Jirsík;Petr Honzík
Affiliations:
Department of Control and Instrumentation, Brno University of Technology, Brno, Czech Republic;Department of Control and Instrumentation, Brno University of Technology, Brno, Czech Republic;Department of Control and Instrumentation, Brno University of Technology, Brno, Czech Republic
Venue:
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
Year:
2010

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Abstract

It is introduced in the paper the newly developed optimization method the Stochastic Optimization Algorithm with Probability Vector (PSV). It is related to Stochastic Learning Algorithm with Probability Vector for artificial neural networks. Both algorithms are inspired by stochastic iterated function system SIFS for generating the statistically self similar fractals. The PSV is gradient method where the direction of individual future movement from the population is based stochastically. PSV was tested on mathematical function minimization and on the travelling sales man problem.