Active target particle swarm optimization

作者：Zhang Ying Nan; Hu Qing Ni; Teng Hong Fei^*

来源：Concurrency and Computation-Practice & Experience, 2008, 20(1): 29-40.

DOI：10.1002/cpe.1207

• particle swarm
• complex method
• optimization
• benchmark functions

We propose an active target particle swarm optimization (APSO). APSO uses a new three-target velocity updating formula, i.e. the best previous position, the global best position and a new target position (called active target). In this study, we distinguish APSO from EPSO (extended PSO)/PSOPC (PSO with passive congregation) by the different methods of getting the active target. Note that here EPSO and PSOPC are the two existing methods for using three-target velocity updating formula, and getting the third (active) target from the obtained positions by the swarm. The proposed APSO gets the active (third) target using complex method, where the active target does not belong to the existing positions. We find that the APSO has the advantages of jumping out of the local optimum and keeping diversity; however, it also has the disadvantages of adding some extra computation expenses. The experimental results show the competitive performance of APSO when compared with PSO, EPSO, and PSOPC.

• 出版日期2008-1
• 单位大连理工大学