A Novel Method for Solving Fuzzy Programming Based on Hybrid Particle Swarm Optimization

Author

Pei, Zhenkui ; Tian, ShengFeng ; Huang, Houkuan

Author_Institution

Beijing Jiaotong Univ., Beijing

fYear

2007

fDate

15-19 Dec. 2007

Firstpage

216

Lastpage

219

Abstract

Fuzzy programming offers a powerful means of handling optimization problems with fuzzy parameters. Fuzzy programming has been used in different ways in the past. The particle swarm optimization (PSO) has been applied successfully to continuous nonlinear constrained optimization problems, neural network, etc. But we have not been found to use PSO for fuzzy programming in literature. In this paper, we combined with fuzzy simulation, neural network and PSO to produce a hybrid intelligent algorithm. Based on this hybrid intelligent algorithm, we introduced for solving fuzzy expected value models. Some numerical examples are given to illustrate the algorithm is effective and powerful.

Keywords

fuzzy set theory; neural nets; particle swarm optimisation; continuous nonlinear constrained optimization problems; fuzzy expected value models; fuzzy programming; fuzzy simulation; hybrid intelligent algorithm; hybrid particle swarm optimization; neural network; Birds; Competitive intelligence; Computational intelligence; Constraint optimization; Dynamic programming; Educational institutions; Fuzzy neural networks; Intelligent networks; Neural networks; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on

Conference_Location

Harbin

Print_ISBN

978-0-7695-3073-4

Type

conf

DOI

10.1109/CISW.2007.4425483

Filename

4425483