An Improved Adaptive Particle Swarm Optimization Algorithm

Because of traditional particle swarm optimization algorithm (PSO) appears premature convergence, and easily falls into local optimization. This paper which refers to the particle swarm optimization algorithm thoughts, and applies the randomness and stable tendency characters of the cloud model for cloud drops, introduces an improved adaptive particle swarm optimization algorithm based on cloud model. The experiment results show that only needs less iterations and gets the more accurate solution, and that the convergence speed, the calculation accuracy and stability is improved obviously and that it has better global search ability and convergence ability.

[1]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[2]  Lin Jianhui.  Cloud Model Based Genetic Algorithm and Its Applications , 2007 .

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Wei Zhenchun,et al.  Adaptive Particle Swarm Optimization Algorithm and Simulation , 2006 .

[5]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..