Personal Best Oriented Constriction Type Particle Swarm Optimization

Author

Chen, Chang-Huang ; Yeh, Sheng-Nian

Author_Institution

Dept. of Electr. Eng., Tung Nan Inst. of Technol., Taipei

fYear

2006

fDate

7-9 June 2006

Firstpage

1

Lastpage

4

Abstract

In this paper, a new search strategy for constriction type particle swarm optimization is presented. The modification is based on the observation that personal past best experience is helpful for searching optimal result. As a result, instead of moving particle to the vicinity of current position, by letting the particle to explore the proximity of personal best position, a great improvement in computation efficiency and quality is achieved. The results are verified through testing on benchmark functions. The advantage of this new scheme is that no extra mathematic operation is introduced compared to those modifications proposed in literature

Keywords

particle swarm optimisation; benchmark functions; constriction type particle swarm optimization; personal best position; search strategy; swarm intelligence; Acceleration; Benchmark testing; Convergence; Evolutionary computation; Genetic mutations; Mathematics; Optimization methods; Particle swarm optimization; Random number generation; optimization; particle swarm optimization; swarm intelligence;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics and Intelligent Systems, 2006 IEEE Conference on

Conference_Location

Bangkok

Print_ISBN

1-4244-0023-6

Type

conf

DOI

10.1109/ICCIS.2006.252300

Filename

4017859