计算机工程与应用Issue(2)：86-91,6.DOI:10.3778/j.issn.1002-8331.1509-0253
自适应迁移预测的动态多目标差分演化算法
Adaptive immigration and prediction strategy based dynamic multi-objective differential evolu-tion
摘要
Abstract
In order to solve the problem of searching and tracing the Pareto Optimal Front(POF)and Pareto Optimal Set (POS), two strategies are investigated. The adaptive immigration strategy is designed to improve the diversity of the popu-lation by adaptively inserting the immigrations according to the changed environments, thus can improve the adaptability to the environments. The prediction strategy is used to quickly trace POF by the prediction population which is estab-lished by the time series and some disturbances. The two strategies are introduced into differential evolution to solve the dynamic multi-objective problems. The experimental results show that the adaptive and prediction strategies based differ-ential evolution shows great ability to adapt to the changed environments and can find POS quickly.关键词
动态多目标优化/自适应迁移策略/预测策略/差分演化算法Key words
dynamic multi-objective optimization/adaptive immigration strategy/prediction strategy/differential evolution分类
信息技术与安全科学引用本文复制引用
万书振..自适应迁移预测的动态多目标差分演化算法[J].计算机工程与应用,2016,(2):86-91,6.基金项目
科技部国家重点科技专项（No.2014ZX07104-005-01）；湖北省教育厅项目（No.B2015253）；湖北省科技厅项目（No.2014CFB681）；三峡大学科研启动基金项目（No.KJ2012B055）。 （）
