Three improved particle swarm optimization algorithms are proposed.
提出了三种改进的微粒群算法。
To solve the equation constrained optimization problems, two new particle swarm optimization algorithms are presented. The experimental results show the algorithms are effective.
提出了求解有等式约束优化问题的两种新粒子群优化算法，数值试验结果表明，算法是有效的。
The computer simulation results showed that the proposed algorithm was superior to original particle swarm optimization algorithms and was effective in separating nonlinear blind sources.
计算机仿真结果表明该算法的收敛性能优于粒子群优化算法，并且在非线性盲信号分离中是有效的。
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。
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。
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。
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。
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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