Predicting Pareto Dominance in Multi-objective Optimization Using Pattern Recognition

Author

Guo Guanqi ; Li Wu ; Yang Bo ; Li Wenbin ; Yin Cheng

Author_Institution

Hunan Inst. of Sci. & Technol., Yueyang, China

fYear

2012

Firstpage

456

Lastpage

459

Abstract

A method of predicting Pareto dominance in multi-objective optimization using pattern recognition, and the framework of Pareto dominance classifier are proposed. A kind of Bayesian classifier is preliminarily implemented. It is used to predict Pareto dominance among the candidate solutions for various typical multi-objective optimization problems. The experimental data and analysis show that the predicted results can be used to recognize the non-dominated candidate solutions. Thus the prediction method of Pareto dominance can serve as an efficient approach for overcoming the curse of computation cost in solving complicated multi-objective optimization problems.

Keywords

Bayes methods; Pareto optimisation; pattern classification; prediction theory; Bayesian classifier; Pareto dominance classifier; Pareto dominance prediction; computation cost; experimental data analysis; multiobjective optimization; nondominated candidate solutions; pattern recognition; Accuracy; Bayesian methods; Classification algorithms; Evolutionary computation; Optimization; Support vector machine classification; Vectors; Bayesian Classifier; Multi-objective Optimization; Pareto Dominance; Pattern Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-1-4577-2120-5

Type

conf

DOI

10.1109/ISdea.2012.589

Filename

6173244