Particle Swarm Optimization Based on a Two-Stage Strategy
Zhanhong Xin · Beijing Youdian Xueyuan xuebao · 2007
A two-stage implementation strategy based on canonical particle swarm optimization was proposed.With the cost of acceptably additional evaluations,this strategy achieved higher success rate which were demonstrated by a suite of benchmark functions.The simulation showed that two-stage implementation strategy could bring forward relatively higher success rate under different upper limitation of iterations.At the same time the proposed strategy reduced the sensitivity of learning rate and presented a stable performance.It was revealed experimentally that the number of sub-populations should be set at a moderate value to consider both the reliability and the computation cost.