An Improved Particle Swarm Optimization Algorithm with Synthetic Update Mechanism

The particle swam optimization algorithm is an effective optimization method for multi-object optimization problem. This paper makes the focus on how to improve the convergence rate and the solution distribution. Thus the synthetic update mechanism is presented in detailed, which is made up of three parts: the first is disturbance operation, the second is mutation operation and the last is gbest value distribution. At last, the simulation results proves that the overall performance of the proposed algorithm is superior to contrast algorithms.