Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid Quantum Evolutionary algorithms (QEA) based on combining QEA with Particle Swarm optimization (PSO).

文章将量子进化算法(QEA)和粒子群算法(PSO)互相结合，提出了两种混合量子进化算法。

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The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.

针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。

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Such algorithms include evolutionary algorithm (EA), particle swarm optimization (PSO), artificial immune system (AIS) and ant colony optimization (ACO) and so on.

这类算法主要包括进化算法(EA)、粒子群算法(PSO)、人工免疫系统(ais)和蚁群算法(aco)等等。

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Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.

针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。

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Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.

针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。

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