An Improved Parallel Adaptive Genetic Algorithm Based on Pareto Front for Multi-objective Problems

Author

Liu, Guangyuan ; Zhang, Jingjun ; Gao, Ruizhen ; Shang, Yanmin

Author_Institution

Sci. Res. Office, Hebei Univ. of Eng., Handan, China

Volume

2

fYear

2009

fDate

Nov. 30 2009-Dec. 1 2009

Firstpage

212

Lastpage

215

Abstract

For multi-objective optimization problems, we introduced IPAGA (Improved Parallel Adaptive Genetic Algorithm) in this paper, a new parallel genetic algorithm which is based on Pareto Front. In this Algorithm, the non-dominated-set is constructed by the method of exclusion. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The results show that the parallel genetic algorithm developed in this paper is efficient.

Keywords

Pareto optimisation; genetic algorithms; parallel algorithms; probability; adaptive-crossover probability; adaptive-mutation probability; evolution population; exclusion method; multiobjective Pareto front problems; non-dominated-set; parallel adaptive genetic algorithm; Concurrent computing; Equations; Genetic algorithms; Genetic engineering; Genetic mutations; Knowledge acquisition; Knowledge engineering; Parallel processing; Pareto optimization; Robustness; Pareto front; adaptive crossover; adaptive mutation; non-dominated-set; parallel genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3888-4

Type

conf

DOI

10.1109/KAM.2009.61

Filename

5362093