Improved Particle Swarm Optimization Algorithm Study Based on static and Dynamic Scale-Free Network Neighborhood Topology
The paper first discusses the PSO improved algorithm based on a static scale-free network topology neighborhood and analyzes the results in different condition of the network's average degree.The results show that the particle optimization performs better when the average degree varies in a wide range.Secondly we briefly introduce the improved algorithm which based on dynamic and directed scale-free network.Taking three different benchmark functions as testing functions,we summary the influence of scale-free network with different average degree.Finally,we analyze the optimization of the particles with different average degree topology structures based on the improved dynamic and directed scale-free network.It shows that the optimization effects is influenced significantly by the average degree especially by the out degree factor,however,it is not necessary better optimization will be made under the higher or lower degree,for example the particles optimization will be better for Rosenbrock function to maintain a smaller average degree,while the impact shows not very different when Rastrigrin function tested with groups of different average degree.