Tracking Changing Extrema with Modified Adaptive Particle Swarm Optimizer

The purpose of this paper is to present a modified PSO (Particle Swarm Optimization) algorithm applied to the complex dynamic environment. The algorithm presented is referred as Improved Adaptive Particle Swarm Optimizer (IAPSO). A new variable-"Activity Factor" and distributed responding method are introduced by IAPSO. Several experiments based on complex dynamic environment were performed to test the performance of the algorithm. The dynamic environment used is generated by the Dynamic Function #1 (DF1). Furthermore, additional feature of setting reinitializing threshold randomly is put to the basic IAPSO to improve its performance. The experimental results indicate that IAPSO is more adaptive in complex dynamic environment than Adaptive Particle Swarm Optimizer (APSO) and other PSO-based algorithms.