An adaptive particle swarm optimization algorithm with new random inertia weight

The paper gives an adaptive particle swarm optimization algorithm with new random inertia weight (RIW-PSO). The new random inertia weight (RIW) is presented by simulated annealing idea to improve the global search ability of PSO and the one to solve the high dimensional and complex nonlinear optimization problems. The PSO with linearly decreasing inertia weight (LDW-PSO) and RIW-PSO are tested with six benchmark functions. The experiments show that the convergent speed and accuracy of RIW-PSO is significantly superior to the one of LDW-PSO.