现代电子技术2017，Vol.40Issue(7)：175-178,4.DOI:10.16652/j.issn.1004-373x.2017.07.046
改进粒子群算法的目标函数变化分类动态优化
Dynamic optimization of objective function changing classification based on improved particle swarm optimization
摘要
Abstract
The objective function and constraint condition for the optimization problem are changed with time,and may change its optimal value. A dynamic optimization of the objective function changing classification based on improved particle swarm optimization is proposed. The dynamic optimization problem is defined to determine the study object of the problem. The classification thought that the objective function is changed with the time varying degree is put forward. The varying function is divided into the types of drastic change,medium grade change and weak change with the monitoring method. Different strategies are adopted for the particle swarm optimization according to the different intensity changes,and integrated for computation. The algorithm was tested with the moving multi-peak problem. The test results show that the improved particle swarm optimization can monitor the changes of the objective function,track the optimal solution momentarily,its average offline error is smaller than that of the standard particle swarm optimization algorithm,and the performance is more stable.关键词
粒子群算法/动态优化/目标函数时变分类/移动峰问题Key words
particle swarm optimization/dynamic optimization/time varying classification of objective function/moving peak problem分类
信息技术与安全科学引用本文复制引用
苏玉,孔国利..改进粒子群算法的目标函数变化分类动态优化[J].现代电子技术,2017,40(7):175-178,4.基金项目
国家青年基金资助项目(61405156) （61405156）
国家863高技术研究发展计划(2012AA101608) （2012AA101608）
河南省科技攻关计划(152102210015) （152102210015）
