Stochastic optimization algorithm with probability vector in mathematical function minimization and travelling salesman problem

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:
WSEAS Transactions on Information Science and Applications
Year:
2010

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Abstract

In the paper there is introduced 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. The influence of the quantity of individuals upon the best achieved fitness function was also tested on the mathematical functions minimization.