A Study of the Multi-objective Evolutionary Algorithm Based on Elitist Strategy

Author

WenBin, Chen ; Yijun, Liu ; Li, Wang ; XiaoLing, Liu

Author_Institution

Sch. of Comput. Sci., Southwest Pet. Univ., Chengdu, China

Volume

1

fYear

2009

fDate

18-19 July 2009

Firstpage

136

Lastpage

139

Abstract

To overcome the decrease of diversity of solutions in NSGA II, a multi-objective evolutionary algorithm based on the elitist strategy, a distribution function is proposed here to improve the elitist strategy. By adjusting the parameters of the distribution function and limiting the elitist solutions, some of the non-elitist solutions will be involved in the genetic computation process. The experimental results show that the improved multi-purpose genetic algorithm has a better diversity and faster convergence of solutions than NSGA II.

Keywords

genetic algorithms; NSGA II; distribution function; elitist strategy; multiobjective evolutionary algorithm; multipurpose genetic algorithm; nondominated sorting genetic algorithm; Computer science; Costs; Distributed computing; Distribution functions; Evolutionary computation; Genetic algorithms; Information processing; Pareto optimization; Petroleum; Production; Distribution Function; Elitist Strategy; Evolving Algorithm; Multi-Objective Optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on

Conference_Location

Shenzhen

Print_ISBN

978-0-7695-3699-6

Type

conf

DOI

10.1109/APCIP.2009.43

Filename

5197015