Title :
A modified mnemonic enhancement optimization method for solving parametric nonlinear programming problems
Author :
Wang, Zhiqiang ; Shao, Zhijiang ; Fang, Xueyi ; Chen, Weifeng ; Wan, Jiaona
Author_Institution :
State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
Abstract :
A mnemonic enhancement optimization framework based on radial basis function (RBF-MEO), which is concerned with the application of RBF interpolation for generation of starting points in parametric nonlinear optimization, is studied in this work. Some theories of interior point algorithm support that the RBF-MEO method is very suitable for collaborating with interior point solvers, such as IPOPT. Numerical experiments illustrate that good accuracy and high rate of convergence are obtained by IPOPT with RBF-MEO.
Keywords :
interpolation; nonlinear programming; radial basis function networks; IPOPT interior point solvers; RBF interpolation; RBF-MEO method; interior point algorithm; mnemonic enhancement optimization method; parametric nonlinear programming; radial basis function; Adaptation model; Biological system modeling; Computational modeling; Feeds; Interpolation; Optimization; Programming;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717333
