An improved quantum-behaved particle swarm optimization algorithm

Author

Yang, Jie ; Xie, Jiahua

Author_Institution

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

Volume

2

fYear

2010

fDate

6-7 March 2010

Firstpage

159

Lastpage

162

Abstract

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithm, which shows good search ability in many optimization problems. In this paper, we present an improved QPSO algorithm, called IQPSO, by combining QPSO and an opposition-based learning concept. Experimental studies on four well-known benchmark problems show that IQPSO achieves better results than QPSO and other variants of PSO on majority of test problems.

Keywords

evolutionary computation; learning (artificial intelligence); particle swarm optimisation; IQPSO; convergence guaranteed algorithm; evolutionary computation; opposition-based learning concept; quantum-behaved particle swarm optimization algorithm; Asia; Automatic control; Benchmark testing; Convergence; Informatics; Particle swarm optimization; Quantum computing; Robot control; Robotics and automation; Sun; evolutionary computation; optimization; particle swarm optimization (PSO); quantum;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on

Conference_Location

Wuhan

ISSN

1948-3414

Print_ISBN

978-1-4244-5192-0

Electronic_ISBN

1948-3414

Type

conf

DOI

10.1109/CAR.2010.5456744

Filename

5456744