THE TRACKING DYNAMICAL PARTICLE SWARM OPTIMIZER FOR DYNAMIC ENVIRONMENTS

摘要：

In this paper, we proposed the Tracking Dynamical Particle Swarm Optimizer (TDPSO) that can efficiently locate and track the optimal solution in a dynamically changing environment. In TDPSO, the particles structure is different from traditional PSO. Each particles knowledge is applied an evaporation constant to gradually weaken the knowledges validity. Through this mechanism, the knowledge of each particle will be gradually updated in a dynamically changing environment. Compared with the traditional PSO, TDPSO can quickly converge to the area of the goal and maintain the shortest distance from the goal.

作者: YING GUI XUE-QIN ZHU WEN-LIN SONG

作者单位: Department of Computer Science and Technology, East China Institute of Technology,,JiangXi Fuzhou 344000,China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3552-3557

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）