A Hybrid Particle Swarm Algorithm for Function Optimization

Author

Yang, Jie ; Xie, Jiahua

Author_Institution

Sch. of Comput. & Inf., Shanghai Second Polytech. Univ., Shanghai, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

Particle swarm optimization (PSO) is one of the evolutionary techniques based on swarm intelligence, which has show good performance in many optimization problems. This paper proposes a new learning strategy to help particles learn experiences from other previous best particles. In order to verify the proposed approach (HPSO), this paper investigates the effects of learning factor on six well-known benchmark functions. Additionally, comparison of HPSO with standard PSO and comprehensive learning PSO shows that HPSO outperforms them on most test functions.

Keywords

biology computing; evolutionary computation; learning (artificial intelligence); particle swarm optimisation; evolutionary techniques; function optimization; hybrid particle swarm optimization; learning strategy; particle experience; swarm intelligence; Benchmark testing; Birds; Educational institutions; Equations; Evolutionary computation; Genetic mutations; Marine animals; Particle swarm optimization; Performance evaluation; Random number generation;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4132-7

Electronic_ISBN

978-1-4244-4134-1

Type

conf

DOI

10.1109/BMEI.2009.5305534

Filename

5305534