Differential Genetic Particle Swarm Optimization for Continuous Function Optimization

Author

Jian, Li

Author_Institution

Dept. of Comput. Eng., Hubei Univ. of Educ., Wuhan, China

Volume

3

fYear

2009

fDate

21-22 Nov. 2009

Firstpage

524

Lastpage

527

Abstract

In this paper, to introduce consistency and diversity, the concept of inertia weight is introduced to a modified genetic particle swarm optimization which was derived from the genetic particle swarm optimization (GPSO) and the differential evolution (DE). The proposed differential genetic particle swarm optimization (DGPSO) is implemented to thirteen well-known constrained optimization functions. And the simulation results have shown the feasibility and effectiveness. Moreover, DGPSO is employed to solve a tension/compression string design problem, and by comparison with the other methods, DGPSO has provided better results.

Keywords

genetic algorithms; particle swarm optimisation; constrained optimization functions; continuous function optimization; differential evolution; differential genetic particle swarm optimization; inertia weight; Application software; Computer science education; Constraint optimization; Continuing education; Genetic engineering; Genetic mutations; Information technology; Particle swarm optimization; Particle tracking; Stochastic processes; differential evolution; global optimization; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on

Conference_Location

Nanchang

Print_ISBN

978-0-7695-3859-4

Type

conf

DOI

10.1109/IITA.2009.33

Filename

5370563