Quantum-behaved particle swarm optimization based on Gaussian disturbance
基于高斯扰动的量子粒子群优化算法

Due to shortcoming of quantum-behaved particle swarm optimization (QPSO) algorithm that it was often premature convergence, this paper proposed a revised QPSO with Gaussian disturbance on the mean best position or global best position of the swarm. The disturbance could effectively prevent the stagnation of the particles and therefore made them escape the local optima more easily. To evaluate the performance of the new method, tested the QPSO with Gaussian disturbance, along with QPSO and standard PSO on several well-known benchmark functions. Experiment simulations show that the proposed algorithm has powerful optimizing ability and more quickly convergence speed.