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A new approach based on support vector machine for solving stochastic optimization
conference contribution
posted on 2013-01-01, 00:00 authored by S Khatami, Abbas KhosraviAbbas Khosravi, Saeid NahavandiMaking 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.
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IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)Pagination
2498 - 2503Publisher
IEEELocation
Manchester, EnglandPlace of publication
Piscataway, N.J.Start date
2013-10-13End date
2013-10-16Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and CyberneticsUsage metrics
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