一种多样性驱动的自适应粒子群优化算法.

Particle swarm optimization algorithm is a stochastic global optimization algorithm, but it is easy to fall into local optima and premature. In order to overcome its defects, a diversity driven adaptive particle swarm optimization（DDA-PSO） algorithm is proposed in this paper. The algorithm includes the attraction stage and the driving stage. In the attraction stage, the linear decreasing inertia weight mechanism is used to accelerate the particle convergence, and in the driving stage, the diversity driving speed strategy is used to improve the population diversity. The two stages adaptively switch to each other, the particles can jump out of the local optima and prevent premature, the algorithm obtains an adaptive balance between the exploration and exploitation. The experimental results show that DDA-PSO algorithm improves the convergence speed and accuracy, and the global search ability is significantly improved. [ABSTRACT FROM AUTHOR]