DocumentCode
3472625
Title
A global search strategy of quantum-behaved particle swarm optimization
Author
Sun, Jun ; Xu, Wenbo ; Feng, Bin
Author_Institution
Sch. of Inf. Technol., Southern Yangze Univ., Wuxi, China
Volume
1
fYear
2004
fDate
1-3 Dec. 2004
Firstpage
111
Abstract
Based on the quantum-behaved particle swarm optimization (QPSO) algorithm, we formulate the philosophy of QPSO and introduce a so-called mainstream thought of the population to evaluate the search scope of a particle and thus propose a novel parameter control method of QPSO. After that, we test the revised QPSO algorithm on several benchmark functions and the experiment results show its superiority.
Keywords
evolutionary computation; learning (artificial intelligence); quantum theory; search problems; QPSO algorithm; benchmark functions; learning inclination point; population mainstream thought; quantum-behaved particle swarm optimization algorithm; search strategy; Equations; Genetic algorithms; Information technology; Linear systems; Organisms; Paints; Particle swarm optimization; Quantum mechanics; Sun; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Cybernetics and Intelligent Systems, 2004 IEEE Conference on
Print_ISBN
0-7803-8643-4
Type
conf
DOI
10.1109/ICCIS.2004.1460396
Filename
1460396
Link To Document