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进化多模态多目标优化与决策算法研究

发布时间:2022-01-04 作者: 浏览次数:
Speaker: 张凯 DateTime: 2022年01月10日(周一)上午10:30-12:00
Brief Introduction to Speaker:

张凯,男,教授,博导,武汉科技大学计算机科学与技术学院副院长。20086月毕业于华中科技大学,获理学博士学位。20086月至20106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TEVCTCYBInformation Science等计算机领域国际权威期刊发表SCI学术论文20余篇。主要研究领域:智能优化算法、多目标优化与决策、DNA计算等。

Place: 六号楼二楼报告厅
Abstract: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 Pa...