Three-Step Optimization Method Based on Posteriori Satisfying Degree for Fuzzy Multiple Objective Optimization

Author

Hu, Chaofang ; Wang, Na

Author_Institution

Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China

fYear

2011

fDate

30-31 July 2011

Firstpage

1

Lastpage

4

Abstract

A three-step satisfying method based on posteriori satisfying degree is proposed for fuzzy multiple objective optimization problem. Firstly, the uniformly distributed Pareto optimal set is acquired by the multiple objective genetic algorithm. Then, the eliminating optimization method is presented to reduce this set to the M-Pareto optimal set. Finally, the fuzzy mean clustering with the validity criteria is used to classify the obtained set to construct the representative M-Pareto optimal subset such that DM can choose the preferred result easily. The numerical example shows the power of the proposed method.

Keywords

Pareto optimisation; fuzzy set theory; genetic algorithms; pattern clustering; M-Pareto optimal set; fuzzy mean clustering; fuzzy multiple objective optimization problem; multiple objective genetic algorithm; posteriori satisfying degree; representative M-Pareto optimal subset; three-step optimization method; uniformly distributed Pareto optimal set; Algorithm design and analysis; Delta modulation; Genetic algorithms; Pareto optimization; Programming;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems Engineering (CASE), 2011 International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4577-0859-6

Type

conf

DOI

10.1109/ICCASE.2011.5997701

Filename

5997701