Convergence Analysis of a Dynamic Discrete PSO Algorithm

摘要：

The particle swarm optimization(PSO)algorithm has exhibited good performance on continuous optimization problems in static environment.However,there are lots of real-world optimization problems that are dynamic and discrete,which is a new research field of PSO.So a dynamic discrete PSO(DDPSO)algorithm is proposed in this paper.In this algorithm,we design a new strategy of environmental monitoring and response.When environment is changed,it can be apperceived by the change of fitness and position of particles and be responded by environment sensitivity and environmental change gene in time.Finally,to analyze the convergence of DDPSO based on the solving of zero state response in discrete-time systems,we get its convergence condition and motion track of particles.As a result,we find that DDPSO has good convergence and diversity of swarm owing to environmental change gene which has randomicity and variability.

关键词： Swarm Intelligence Particle Swarm Optimization Convergence Discrete-Dynamic Environment

作者: LUO Guilan ZHAO Hai SONG Chunhe

作者单位: Province Key Laboratory of Embedded Technology,Northeastern University,Shenyang,China

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）