A Combine Historical Global and Local Best Particle Swarm Optimization Algorithm

A new particle swarm optimization algorithm was proposed to increase the diversity of the shared information. In the process of velocity updating, the historical global best in the previous rounds was combined with the local best in the current round to increase the diversity of information. In addition, according to the different combining ways of two kinds of information, the basic algorithm was extended to 3 kinds of extension algorithm. Simulation results on 6 typical functions showed that the improved particle swarm algorithm can efficiently overcome the premature of standard particle swarm algorithm.