Stochastic Optimization Problem through Particle Swarm Optimization Algorithm

Author

He, Fangguo ; Chen, Wenlue

Author_Institution

Coll. of Math. & Inf. Sci., Huanggang Normal Univ., Huanggang

Volume

2

fYear

2009

fDate

25-26 April 2009

Firstpage

692

Lastpage

695

Abstract

In this paper, two classes of stochastic optimization problems, which are expected value models and chance-constrained programming, are introduced. In order to solve the problems, the method of stochastic simulation is used to generate training samples for neural network, and then particle swarm optimization algorithm and neural network are integrated to produce a hybrid intelligent algorithm. Two numerical examples are provided to illustrate the effectiveness of the hybrid particle swarm optimization algorithm.

Keywords

constraint handling; learning (artificial intelligence); neural nets; particle swarm optimisation; stochastic programming; chance constrained programming; neural network; particle swarm optimization; stochastic optimization problem; training sample; Constraint optimization; Functional programming; Intelligent networks; Modeling; Neural networks; Optimization methods; Particle swarm optimization; Stochastic processes; Stochastic systems; Uncertainty; Neural network; Particle swarm optimization; Stochastic optimization; Stochastic simulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-1-4244-4223-2

Type

conf

DOI

10.1109/NSWCTC.2009.355

Filename

4908563