An improved particle swarm optimization and its application on web service composition
Lou Yuan-sheng, Po Hu, Tao Fu-ling · 2010
As the particle swarm optimization (PSO) algorithm has some deficiencies such as slow convergence and easy to fall into the local extreme value in some circumstances, this paper presents an improved particle swarm optimization with a new inertia weight. In different stages of the algorithm run, a corresponding formula is used to calculate the inertia weight. In Addition, adaptive mutation and linear-changed learning factor are introduced in this paper. Then the relational test simulation is carried out, and the simulation results shows that the improved algorithm is feasible and efficient. Finally, this paper attempts to solve the web service composition optimization with the improved algorithm.