Application of improved TOPSIS method based on ACO and BP algorithm

Author

Niu, Dongxiao ; Lv, Jialiang

Author_Institution

Sch. of Bus. Adm., North China Electr. Power Univ., Baoding

fYear

2008

fDate

25-27 June 2008

Firstpage

6183

Lastpage

6186

Abstract

TOPSIS method is applied abroad in the decision and evaluate field, this article introduce the ACO algorithm which is based on continuous space optimization object to improve traditional TOPSIS method, so as to increase the order precision by search the optimization index weights. Then the BP algorithm is used in establishing the relationship between sample data and the result of evaluation, consequently the evaluate efficiency is increased by the emulator model which can evaluate more new sample directly. The demonstration shows that the two methods above can take great effect in TOPSIS evaluation.

Keywords

backpropagation; neural nets; optimisation; ACO algorithm; ant colony optimization; backpropagation neural nets; continuous space optimization; improved TOPSIS method; optimization index weights; technique for order preference by similarity to an ideal solution; Automation; Decision support systems; Intelligent control; Virtual manufacturing; Virtual reality; ACO method; BP neural network; Index weight; TOPSIS method;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4592795

Filename

4592795