Constructing a Markov Chain on Particle Swarm Optimizer

Author

Chou, Chao-Wei ; Lin, Jiann-Horng ; Yang, Chorng-Horng ; Tsai, Hsien-Leing ; Ou, Ya-Hui

Author_Institution

Dept. of Inf. Manage., I-Shou Univ., Kaohsiung, Taiwan

fYear

2012

fDate

26-28 Sept. 2012

Firstpage

13

Lastpage

18

Abstract

The Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. As far as our investigation, most of the relevant researches are based on computer simulations and seldom of them are based on theoretical approach. In this paper, theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this paper, which contains all the information needed for the future evolution. Then the memory-less property of the state defined in this paper is investigated. Finally, by using the concept of the state and suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov chain is established.

Keywords

Markov processes; particle swarm optimisation; PSO stochastic behavior; complex stochastic process; computer simulations; memory-less property; particle swarm optimizer; stationary Markov chain; theoretical approach; Convergence; Genetic algorithms; Indexes; Markov processes; Optimization; Particle swarm optimization; Vectors; Markov chain; particle swarm optimizer;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Bio-Inspired Computing and Applications (IBICA), 2012 Third International Conference on

Conference_Location

Kaohsiung

Print_ISBN

978-1-4673-2838-8

Type

conf

DOI

10.1109/IBICA.2012.59

Filename

6337704