A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
Considering that the particle swarm optimization (PSO) algorithm is quite simple and easy to implement, it was used to estimate the nonlinear model parameters in this paper.
粒子群算法操作简便、容易实现且全局搜索功能较强，适用于非线性参数估计。
Particle swarm optimizers are a simple stochastic glob al optimization technique.
粒子群优化算法是一类简单有效的随机全局优化技术。
Particle swarm optimization (PSO) algorithm is a new intelligent one with simple principle which is easy to implement.
微粒群算法是一种新型的智能优化算法，有较强的发现最优解的能力，且算法简单，易于实现。
Restrictive assumptions on utility function are removed and a simple distributed rates algorithm is proposed using the particle swarm optimization based on the network utility maximization framework.
基于最大化用户效用函数框架，去掉了以往研究中对效用函数的严格假设，利用粒子群方法设计了分布式速率控制算法。
Restrictive assumptions on utility function are removed and a simple distributed rates algorithm is proposed using the particle swarm optimization based on the network utility maximization framework.
基于最大化用户效用函数框架，去掉了以往研究中对效用函数的严格假设，利用粒子群方法设计了分布式速率控制算法。
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