Conference Proceeding

An improved quantum-behaved particle swarm optimization algorithm

Sch. of Comput. & Inf., Shanghai Second Polytech. Univ., Shanghai, China
04/2010; DOI:10.1109/CAR.2010.5456744 pp.159 - 162 In proceeding of: Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, Volume: 2
Source: IEEE Xplore

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.

0 0
 · 
0 Bookmarks
 · 
43 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

Experimental studies
 
global convergence
 
improved QPSO algorithm
 
IQPSO
 
optimization problems
 
PSO
 
QPSO
 
Quantum-behaved particle swarm optimization
 
shows good search ability