Abstract: The purpose of this paper is to present a modified Particle Swarm Optimization（PSO） algorithm applied to the complex dynamic environment.The method presented is defined as Improve Niche Particle Swarm Optimizer（IN-PSO）.It can improve the reliability and accuracy while tracking dynamic pole in dynamic environment and avoid converge to a optimality.The environment used in these experiments is generated by Dynamic Function # 1（DF1）.The results of the experiments elucidate that IN-PSO is more adaptive than Adaptive Particle Swarm Optimizer（APSO）.

Key words: dynamic environment, niche, PSO, DF1

摘要： 为了提高在动态环境下追踪变化的极点的可靠性和精确性的能力，避免算法收敛于一个最优解，提出了一种改进的小生境微粒群算法。使用DF1（Dynamic Function 1）生成的复杂动态环境对这种算法进行了验证，并与经典的APSO（Adaptive Particle Swarm Optimizer）算法进行了对比，实验结果表明了该算法的有效性。

关键词: 动态环境, 小生境, 微粒群算法, DF1