Particle Swarm Optimizer Based on Diversity of Particle in Dynamic Environments

To the shortages existing among the current environmental detection and response techniques,an improved detection method based on the particle information was developed,which not only could reduce the optimization cost but also could make up the limitation of the usual detection methods.At the same time,a new response method combined with population diversity and escaping behavior was designed.The improved detection and response algorithms were applied to solve various parabola functions with complex dynamic changes,and the simulation results show the proposed algorithm is effective in dynamic environments.