An Evolutionary Algorithm for Uncertain Optimization Problems

Author

He, Fangguo ; Shao, Guiming

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

In order to model uncertain optimization problems, fuzzy expected value programming and chance-constrained programming are formulated. Firstly, the method of fuzzy simulation is used to generate training samples for neural network, and the neural network is embedded into PSO to design a hybrid intelligent algorithm. Then, the death penalty function is adopted to deal with the constraints. Finally, some numerical examples are provided to illustrate the feasibility and effectiveness of the algorithm.

Keywords

constraint theory; evolutionary computation; fuzzy set theory; learning (artificial intelligence); mathematical programming; neural nets; particle swarm optimisation; chance-constrained programming; death penalty function; evolutionary algorithm; fuzzy expected value programming; fuzzy simulation; hybrid intelligent algorithm; neural network training; particle swarm optimisation; uncertain optimization problems; Chromium; Evolutionary computation; Fuzzy neural networks; Genetic programming; Mathematical programming; Modeling; Neural networks; Particle swarm optimization; Stochastic processes; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5366513

Filename

5366513