粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 -Particle swarm optimization (PSO) is a kind of stochastic optimization technique based on population. Compared with other evolutionary algorithms based on the group, they are initialized to a set of random solutions. The difference is: follow the principle of survival of the fittest evolutionary computation, and PSO simulation of society. Each of the possible solutions is expressed as a particle in the swarm, each particle has its own position vector and velocity vector, and the fitness of a target is determined by the target function. All particles in the search space to a certain speed, by following the current search to find the optimal value to find the global optimal value.