To overcome the shortcomings the traditional particle swarm optimization algorithm (PSO)

such as poor ability to escape a local optimal

premature convergence and low precision

we proposed a new PSO based on multiscale-selective-learning and detecting-shrinking strategies

which called MDPSO in short.In the multiscale-selective-learning strategy

a particle executes a multiscale learning process to improve its studying efficiency by adopting its topology

selecting neighbors

and choosing target variable dimensions.In the detecting-shrinking strategy

particles' historical best solutions are periodic sampling and some useful information

which extracting from the sampling results

is used to direct the best solutions to carry out a detecting operation.The aims of the strategy are to improve PSO's global searching ability and to help the population escape a local optimal solution.While the best solution situating around a global optimal solution

the algorithm implements the shrinking strategy to confine the search space to a small one the aims of which are to improve the PSO's exploitation ability and to increase the accuracy of the solutions.The proposed method was applied to twenty-two typical benchmark functions

and the comparisons of the performance between MDPSO and other eight PSO algorithms were experimented.The results suggest that the proposed strategies can effectively overcome the premature convergence

speed up the convergence and improve solutions accuracy.