An Improved Multi-objective Genetic Algorithm Based on Granular Ranking and Distant Reproduction

Author

Yan Tai-shan ; Guo Guan-qi ; Li Wu

Author_Institution

Sch. of Inf. & Commun. Eng., Hunan Inst. of Sci. & Technol., Yueyang, China

Volume

1

fYear

2011

fDate

24-25 Sept. 2011

Firstpage

151

Lastpage

154

Abstract

In order to improve the performance of multi-objective genetic algorithms, an improved multi-objective genetic algorithm based on granular ranking and distant reproduction (IMOGA) is proposed. In this algorithm, the concept granularity is introduced into multi-objective ranking, and a selection method based on granular ranking is used. Meanwhile, the consanguinity feature is fused into individuals, and a crossover method based on distant reproduction is used. Experiments were taken on multi-objective functions with constraints, the validity of IMOGA was proved. Compared with several multi-objective optimization algorithms such as NSGAII, MOQCGA and MOPSO, the solutions of IMOGA are more excellent, and its robustness is better.

Keywords

genetic algorithms; MOPSO; MOQCGA; NSGAII; consanguinity feature; crossover method; distant reproduction; granular ranking; improved multiobjective genetic algorithm; multiobjective optimization algorithms; selection method; Computers; Evolutionary computation; Genetic algorithms; Information systems; Pareto optimization; Vectors; Distant reproduction; Granular ranking; Multi-objective genetic algorithm; Multi-objective optimization; Pareto optimal;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on

Conference_Location

Nanjing, Jiangsu

Print_ISBN

978-1-4577-1419-1

Type

conf

DOI

10.1109/ICM.2011.66

Filename

6113378