Title :
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
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;
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
DOI :
10.1109/ISdea.2012.589
