A parallel genetic algorithm in multi-objective optimization

Author

Wang Zhi-xin ; Ju, Gang

Author_Institution

Sch. of Energy & Environ., Southeast Univ., Nanjing, China

fYear

2009

fDate

17-19 June 2009

Firstpage

3497

Lastpage

3501

Abstract

Based on the combination of NSGA-II algorithm and parallel genetic algorithm, this paper presents a parallel genetic algorithm for multi-objective optimization (PNSGA). At the evolving process of this new algorithm, an individual migration to improve the parallel searching speed is applied to improve the efficiency of this algorithm and the accuracy of Pareto optimal set; at the same time, an individual update strategy is introduced to keep the diversity of Pareto optimal set. Data show that the Pareto optimal solutions or the solution candidates output by PNSGA that are scattered extensively and uniformly.

Keywords

Pareto optimisation; genetic algorithms; parallel algorithms; set theory; NSGA-II algorithm; Pareto optimal set; multiobjective optimization; parallel genetic algorithm; parallel searching speed; Convergence; Diversity reception; Genetic algorithms; Genetic engineering; Parallel algorithms; Parallel processing; Pareto optimization; Performance evaluation; Scattering; Sorting; Individual migration; Individual update; Multi-objective optimization; NSGA-II; Parallel genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192490

Filename

5192490