Title :
An application of quantum-inspired particle swarm optimization to function optimization problems
Author :
Tazuke, Koichiro ; Muramoto, Noriyuki ; Matsui, Nobuyuki ; Isokawa, T.
Author_Institution :
Grad. Sch. of Eng., Univ. of Hyogo, Himeji, Japan
Abstract :
Quantum-Inspired Particle Swarm Optimization (QPSO) is an extension of Particle Swarm Optimization (PSO) methods, in which the concept of quantum mechanics is adopted. The state of a particle in QPSO is described by a wave function derived from the Schrödinfer equation, whereas a particle in standard PSOs has its location and velocity as its state. The performances of QPSOs are demonstrated through the optimization problem for higher-dimensional functions, with comparison of the standard PSO. The experimental results show that QPSOs can find (near) optimal values much faster than the conventional PSO.
Keywords :
particle swarm optimisation; quantum computing; quantum theory; QPSO; Schrödinger equation; function optimization problems; quantum mechanics; quantum-inspired particle swarm optimization; wave function; Density functional theory; Optimization; Particle swarm optimization; Probabilistic logic; Standards; Wave functions;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6706880