時間：2021年11月04日15:00

地點：崇真樓南樓A4030

報告題目：求解多模態多目標優化問題的進化算法研究

報告人姓名：張凱

報告人單位：武漢科技大學k8凯发国际科學與技術學院

報告摘要：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 s 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.

報告人簡介：

張凱，男，教授，博導，武漢科技大學k8凯发国际科學與技術學院副院長。2008年6月畢業於華中科技大學，獲理學博士學位。2008年6月至2010年6月在北京大學信息學院從事博士後研究工作。2017年國家留學基金委公派訪學。現任中國電子學會生物計算專委會常務理事、湖北省運籌學會常務理事，武漢k8凯发国际軟件工程學會理事。榮獲2015年度湖北省優秀博士後，2014年度武漢市優秀青年科技工作者。主持國家自然科學基金3項，湖北省自然科學基金1項，獲得湖北省科技進步二等獎2項，出版學術專著1部，在TEVC，TCYB，Information Science等k8凯发国际領域頂級期刊發表SCI學術論。