时间：2021年11月04日15:00
地点：崇真楼南楼A4030
报告题目：求解多模态多目标优化问题的进化算法研究
报告人姓名：张凯
报告人单位：武汉科技大学计算机科学与技术学院
报告摘要：In recent years, numerous efficient and effective multimodal multi-objective evolutionary algorithms (MMOEAs) have been developed to search for multiple equivalent sets of Pareto optimal solutions simultaneously. However, some of the MMOEAs prefer convergent individuals over diversified individuals to construct the mating pool, and the individuals with slightly better decision space distribution may be replaced by significantly better objective space distribution. Therefore, the diversity in the decision space may become deteriorated, in spite of the decision and objective diversities have been taken into account simultaneously in most MMOEAs. Because the Pareto optimal subsets may have various shapes and locations in the decision space, it is very difficult to drive the individuals converged to every Pareto subregion with a uniform density. Some of the Pareto subregions may be overly crowded, while others are rather sparsely distributed. Consequently, many existing MMOEAs obtain Pareto subregions with imbalanced density. In this paper, we present a two-stage double niched evolution strategy, namely DN-MMOES, to search for the equivalent global Pareto optimal solutions which can address the above challenges effectively and efficiently. The proposed DN-MMOES solves the multimodal multi-objective optimization problem (MMOP) in two stages. The first stage adopts the niching strategy in the decision space, while the second stage adapts double niching strategy in both spaces. Moreover, an effective decision density self-adaptive strategy is designed for improving the imbalanced decision space density. The proposed algorithm is compared against eight state-of-the-art MMOEAs. The inverted generational distance union (IGDunion) performance indicator is proposed to fairly compare two competing MMOEAs as a whole. The experimental results show that DN-MMOES provides a better performance to search for the complete Pareto Subsets and Pareto Front on IDMP and CEC 2019 MMOPs test suite.
报告人简介：
张凯，男，教授，博导，武汉科技大学计算机科学与技术学院副经理。2008年6月毕业于华中科技大学，获理学博士学位。2008年6月至2010年6月在北京大学信息学院从事博士后研究工作。2017年国家留学基金委公派访学。现任中国电子学会生物计算专委会常务理事、湖北省运筹学会常务理事，武汉计算机软件工程学会理事。荣获2015年度湖北省优秀博士后，2014年度武汉市优秀青年科技工作者。主持国家自然科学基金3项，湖北省自然科学基金1项，获得湖北省科技进步二等奖2项，出版学术专著1部，在TEVC，TCYB，Information Science等计算机领域顶级期刊发表SCI学术论。