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. Attached Files Name Description MIMEType Size Downloads Title A new approach based on support vector machine for solving stochastic optimization Author(s) Khatami, Seyed A. Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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. 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   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 09 Dec 2013, 10:45:43 EST Mon, 09 Dec 2013, 11:19:41 EST Tue, 10 Dec 2013, 13:24:56 EST Mon, 17 Feb 2014, 15:07:16 EST Tue, 18 Feb 2014, 08:16:19 EST Mon, 11 Aug 2014, 10:13:12 EST Fri, 17 Oct 2014, 12:31:25 EST Wed, 21 Oct 2015, 14:56:35 EST Sat, 15 Apr 2017, 02:35:27 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 345 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 09 Dec 2013, 10:45:43 EST

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