Adaptive particle swarm optimization algorithm with dynamically changing inertia weight

To overcome the premature caused by standard particle swarm optimization (PSO) algorithm searching for the large lost in population diversity, an adaptive PSO with dynamically changing inertia weight is proposed. The average of similarity of particles in the population as the measure of population diversity is introduced into proposed algorithm to balance the trade-off between exploration and exploitation. A function relationship between inertia weight and the measure of population diversity is established by analyzing the dynamically relationship between them, which is embedded into the algorithm. The simulation results show that the algorithm has better probability of finding global optimum and mean best value, especially for multimodal function.