Adaptive Double Layers Particle Swarm Optimization Algorithm

The particle swarm optimization (PSO) has some shortcomings, such as premature convergence and low convergence speed in the late evolutionary. An improved algorithm is proposed which is Adaptive Double Layer Particle Swarm Optimization algorithm(ADLPSO).In each population iteration, the algorithm takes advantage of the characteristic of memory swarm to tent to the global optimal position on double layer particle swarm, memory swarm being updated by using an improved the memory particle swarm update formula. At the same time, in order to improving the diversity of the population, it uses an improved adaptive adjustment strategy to update inertia weight and makes the adaptive process and the update of the double layer particle swarm synchronization. The experiment results show that that the new algorithm is more fast to find better solution.