中文摘要：

提出了一种适应复杂动态环境的微粒群算法--改进的自适应微粒群算法（Improved AdaptiveParticle Swarm Optimizer,IAPSO）.使用由DF1（Dynamic Function 1）生成的复杂动态环境对IAPSO算法进行了验证,并着重将IAPSO算法同APSO（Adaptive Particle Swarm Optimizer）算法进行了对比.实验结果证明,在复杂的动态环境中,IAPSO算法比APSO算法具有更好的适应性.

英文摘要：

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 Improved Adaptive Particle Swarm Optimizer （IAPSO）. A number of experiments are performed to test the performance of the IAPSO. The environment used in the experiments is generated by Dynamic Function # 1 （DF1）. The results of the experiments indicate that IAPSO is more adaptive than Adaptive Particle Swarm Optimizer（APSO）.