Improvement on Parallel AQPSO Using the Best Position

Author

Ma, Yun ; Liu, Yang ; Yang, Deyun ; Chen, Yuping

Author_Institution

Coll. of Inf. Technol., TaiShan Univ., Tai´´an

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

825

Lastpage

828

Abstract

Quantum-behaved Particle Swarm Optimization (QPSO) is a new particle swarm optimization (PSO) algorithm. Compared with standard PSO (SPSO), it guarantees that particles converge in global optimum point in probability and this algorithm has better performance and stability. This paper introduces an improved Adaptive QPSO algorithm, puts the parallelisms crude of AQPSO and high speed of computer together, and island model is introduced. Multiswarm Parallel AQPSO (PAQPSO) Algorithm is reported. The algorithm employs the co-evolution model to avoid pre-maturity and improves global search performance. This approach is tested on several accredited benchmark functions and the experiment results show much advantage of PAQPSO to PSOs, and the running time is also decreased in linear.

Keywords

parallel algorithms; particle swarm optimisation; probability; quantum computing; search problems; co-evolution model; global search performance; multiswarm parallel adaptive QPSO algorithm; probability; quantum-behaved particle swarm optimization; Conferences; Data mining; Co-evolution; QPSO; adaptive; parallel;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Conference_Location

Moscow

Print_ISBN

978-0-7695-3543-2

Type

conf

DOI

10.1109/WKDD.2009.145

Filename

4772062