In view of the limitation of the current kinds of particle swarm optimization(PSO) algorithm, a self-adaptive
learning of hybrid strategy algorithm based on parallel particle swarm optimization(HLPSO) is proposed. The algorithm
combines four strategies reasonably in the different point of view: Convergence, jump out, exploration and exploitation,
which chooses an appropriate strategies to solve the different forms of problems through adjusting the probability of the
strategies gradually in the process of optimizing. Moreover, simulation experiment on a suite of 7 benchmark functions
is given, and the comparisons with other algorithms are provided. The results show that the proposed approach has better
convergence rate and great capability of preventing premature convergence.