Two Novel Particle Swarm Optimization Algorithm Models
Shengli Song, Li Kong, Jingjing Cheng · 2009
According to the intelligent behavior of social population, two novel particle swarm algorithm optimization models are proposed by enhancing collaboration and information sharing capabilities of individuals. Benchmark function simulation results show the new algorithms, with both a better stability and a steady convergence, not only enhance the local searching efficiency and global searching performance greatly, but also have faster convergence speed and higher precision, and can avoid the premature convergence problem effectively.