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An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position. (English) Zbl 1157.65395
Summary: Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence algorithm, which outperforms the original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according to the fitness values of the particles. It is shown that the improved QPSO has faster local convergence speed, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. The proposed improved QPSO, called weighted QPSO (WQPSO) algorithm, is tested on several benchmark functions and compared with QPSO and standard PSO. The experiment results show the superiority of WQPSO.

MSC:
65K05 Numerical mathematical programming methods
90C15 Stochastic programming
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