Particle Swarm Optimization with Highly-Clustered Scale-Free Network Model
Jing Li · Fuza xitong yu fuzaxing kexue · 2010
Enlightened by the properties of scale-free network model,preferential attachment mechanism of the BA model is extended and introduced into particle swarm optimization,and a novel particle swarm optimization with highly-clustered scale-free network model(PSO-HCSF) is proposed.At the early stage of the algorithm,particles were randomly distributed in a ring,new particles are continuously added into the population with searching,and based on the node degree and the distance between nodes new connections are produced,and a high aggregation degree of scale-free network model are formed in the end.In this way,the majority of particles search in local scope and a small amount of particles search with the overall pattern,two ways check and balance.Experimental simulations show that the new method obtains better evolution speed and convergence performance.