Abstract
Quantum-behaved particle swarm optimization algorithm (QPSO) was proposed as a kind of swarm intelligence, which outperformed standard particle swarm optimization algorithm (PSO) in search ability. This paper presents an improved QPSO with nonlinear controlled parameter according to the fitness value of the particles. Simultaneously, we apply the improved QPSO to solve the problems of target position measurement. The experimental results show that the improved QPSO has faster convergence speed, higher measurement accuracy, and good localization performance.
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