粒子群优化（PSO）是一种基于群体智能的优化算法。

Particle Swarm Optimization (PSO) is an intelligent optimization algorithm based on the Swarm Intelligence.

本算法是基于Kennedy和Eberhart于1995年开发的一个简单数学模型来描述鸟类和鱼类的社群行为。

It is based on a simple mathematical model, developed by Kennedy and Eberhart in 1995, to describe the social behavior of birds and fish.

该模型主要依赖于描述复杂系统动力学的自组织基本原理。

The model relies mostly on the basic principles of self-organization which is used to describe the dynamics of complex systems.

群体智能可以使得此类系统实现更高水平智能的能力，而任何系统单元都绝对无法实现。

Swarm intelligence is ability of such systems, to achieve a higher level of intelligence, which is absolutely unreachable for any of system units.

例如，鸟类群体作为一个社群，有着非常复杂的行为模式，这当然超出了群体中任何鸟类的智力水平。

For example, a flock of birds as a society, has very complex behavior patterns, which is beyond the intelligence level of any of birds in the flock, of course.

然而，这种复杂的模式是通过简单而重复的任务创建的，由群体中的任何成员予以执行。

However, this complex patterns are created via simple and repetitive tasks, performed by any of members in the flock.

PSO采用了一个非常简单的社会行为模型，在合作和智能的框架中解决优化问题。

PSO utilizes a very simplified model of social behavior to solve the optimization problems, in a cooperative and intelligent framework.

粒子群算法是最有用、最著名的元启发式算法之一，已成功地应用于各种优化问题。

PSO is one of the most useful and famous metaheuristics and it is successfully applied to various optimization problems.

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