A new approach based on support vector machine for solving stochastic optimization

Khatami, Seyed A., Khosravi, Abbas and Nahavandi, Saeid 2013, A new approach based on support vector machine for solving stochastic optimization, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 2498-2503.

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Title A new approach based on support vector machine for solving stochastic optimization
Author(s) Khatami, Seyed A.
Khosravi, Abbas
Nahavandi, Saeid
Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Conference location Manchester, England
Conference dates 13-16 Oct. 2013
Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Systems, Man and Cybernetics Conference
Start page 2498
End page 2503
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) dependent chance programming
support vector machine regression
Monte-Carlo simulation
genetic algorithm
Summary Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.
ISBN 9781479906529
9780769551548
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058812

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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