The loss of population diversity is an important reason to cause the premature convergence in particle swarm
optimization(PSO). Therefore, this paper makes the probabilistic characteristics analysis of learning parameters of PSO
and proposes the relationship between the loss of population diversity and the probabilistic distribution and dependence
of learning parameters. Then an adaptive learning particle swarm optimization(ALPSO) is proposed, where the modified
probabilistic distribution of learning parameters is used to maintain population diversity. Meanwhile, the adaptive learning
parameter with changing of evolutional state is designed to balance the global and local search abilities of particles.
Experimental results show that ALPSO improves the convergence precision and effectively avoids the premature
convergence.