中文说明：普通粒子群算法无法感知外界环境的变化，在外界环境发生改变时无法实时进行响应，因而缺乏动态环境寻优能力。在普通粒子群算法基本上通过增加敏感粒子得到一种动态粒子群算法，该算法通过实时计算敏感粒子的适应度值从而感知外界环境的变化，当外界环境的变化超过一定的阈值时算法以按一定比例更新速度和粒子的方式进行相应，从而具有动态环境寻优的功能。本案例研究了基于动态粒子群算法的动态环境寻优算法。
English Description：
General Particle Swarm algorithm cannot perceive the external environment changes, respond to the external environment is not readily changed, and thus lack a dynamic environment optimization capability. General Particle Swarm Optimization algorithm by adding sensitive particles are basically a dynamic Particle Swarm algorithm the algorithm by calculating in real time-sensitive particles fitness and perceived changes in the external environment, when the external environment changes exceeds a certain threshold algorithm for proportionally update speed and particle accordingly, which has a dynamic environment optimizing functions. This case study is based on Particle Swarm Optimization algorithm for dynamic environments.