Abstract: For high dimensional optimization problems, convergence of different dimension is different to random initialization of particle swarm optimization algorithm, the commonly used inertia weight can not balance the global search and local search very well and the algorithm is easy to fall into local optimum. This article introduces a new particle swarm optimization algorithm based on dimension sine changing inertia weight and dimension mutation in line with t distribution which taking convergence of different dimension into account and keeping the population diversity very well. Experimental studies are carried out on four classical functions, and the computational results show that the algorithm has faster convergence speed and higher convergence accuracy and rate than traditional article swarm optimization algorithm.

Key words:  particle swarm optimization, dimension sine changing, t distribution, dimension mutation, diversity