Title :
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
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;
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
DOI :
10.1109/ICM.2011.66
