DocumentCode :
2249396
Title :
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
Link To Document :
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