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A grey prediction-based evolutionary algorithm for dynamic multiobjective optimization
2020-09-27 11:11     (点击: )

报告题目2:A grey prediction-based evolutionary algorithm for dynamic multiobjective optimization

报告人:汪春峰博士

报告时间:2020年9月28日17:20~18:20

报告地点:理工教学楼阶梯教室

报告摘要:Dynamic multiobjective optimization problems (DMOPs) usually includemultiple conflicting objectives that change over time. A good evolutionary algorithm should be able to quickly track of the moving Pareto optimal front (POF) and Pareto optimal set (POS) over time. To solve DMOPs, a predictive method is proposed herein based on grey prediction model, which is composed of three essential ingredients. The first one is that the population is divided into multiple clusters, which can help the population to preserve diversity throughout the evolutionary process. The second one is that the individuals used to detect environmental changes are taken from different clusters, which in turn help the proposed algorithm to detect environmental changes more promptly and accurately. The third one is to build the grey prediction model by using the centroid point of each cluster when detecting the environmental change, and then generate the initial population. Empirical results show that the proposed algorithm can deal with dynamic environments and track the varying POS and POF effectively and efficiently, and achieve better performances on most test problems than several selected state-of-the-art algorithms.

报告人简介:汪春峰,博士,副教授,硕士研究生导师,留学归国人员。2006年至今工作于河南师范大学。2014年11月-2017年2月在上海大学数学学科博士后流动站工作,2018年-2019年在美国俄克拉荷马州立大学访学。主持完成国家自然科学基金1项,主持完成河南省科技攻关项目1项,河南省高等学校重点科研项目1项,中央高校基本科研业务项目资助1项,参与完成国家自然科学基金3项;截止目前发表学术论文近50篇,其中以第一作者发表SCI和EI论文26篇。获河南省科技进步三等奖1项,河南省教育厅优秀科技论文一等奖1项。

                                     数学与计算机科学学院

                                        2020年9月28

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