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微粒群算法,是一种新型的进化计算方法,在不同领域有着广泛应用。通过研究基本微粒群算法发现,计算过程应用测度为零的线性进行搜索过程中,基本群算法会较早的进入早收敛现象,因此提出了改进版的微粒群算法,该算法在运算过程中可将极限位置进行动态化的调整,使得各个微粒的极限位置在其线性运动后形成动态圆形分布。对改进版的微粒群算法进行实例仿真,证明了其方法的可靠性和有效性。
Particle swarm optimization is a new type of evolutionary computation method and has been widely used in different fields. By studying the basic particle swarm optimization algorithm, it is found that the basic swarm optimization algorithm will enter early convergence in the process of linear search with zero measure. Therefore, an improved particle swarm optimization algorithm is proposed, which can be used in the computation process The limit position of the dynamic adjustment, so that the ultimate location of each particle in the linear movement of the formation of dynamic circular distribution. The improved particle swarm optimization algorithm is used to simulate the simulation and the method is proved to be reliable and effective.