DocumentCode :
3043593
Title :
A New Approach Based on Support Vector Machine for Solving Stochastic Optimization
Author :
Khatami, Seyed Amin ; Khosravi, Abbas ; Nahavandi, S.
Author_Institution :
Comput. Sci. & IT Dept., Islamic Azad Univ., Fars, Iran
fYear :
2013
fDate :
13-16 Oct. 2013
Firstpage :
2498
Lastpage :
2503
Abstract :
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.
Keywords :
genetic algorithms; mathematical programming; regression analysis; stochastic processes; support vector machines; DCP model; SVM regression; chance function; dependent chance programming; genetic algorithm; hybrid intelligent algorithm; stochastic optimization; support vector machine regression; Bioinformatics; Genomics; Mathematical model; Optimization; Programming; Stochastic processes; Support vector machines; Monte-Carlo simulation; dependent chance programming; genetic algorithm; support vector machine regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
Type :
conf
DOI :
10.1109/SMC.2013.426
Filename :
6722179
Link To Document :
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