摘要
如何有效评价个体是处理高维多目标优化问题的关键.文中提出改进的反世代距离(IGD+S)指标,以反世代距离(IGD)指标为原型,融合修改的反世代距离(IGD+)指标的弱支配性,增加无贡献个体概念,可综合评价解集收敛性和多样性.将IGD+S指标嵌入进化算法框架中,提出基于IGD+S指标的高维多目标进化算法.在环境选择过程中,根据IGD+S选择优良个体.实验表明,文中算法在处理DTLZ问题和WFG问题上具有良好的竞争力.
How to evaluate solutions effectively is a key to solving many-objective optimization problem.An inverted generation distance(IGD + S) indicator is proposed based on IGD indicator, incorporating weak dominance of IGD + indicator and employing the concept of non-contributing individuals.Convergence and diversity of solution set are evaluated comprehensively.IGD + S indicator is embedded in the framework of evolutionary algorithms, and a multi-objective evolutionary algorithm based on IGD + S indicator is presented.In the process of environmental selection, excellent solutions are selected according to enhanced IGD + S indicator.Experimental results demonstrate that the proposed algorithm is competitive in DTLZ problems and WFG problems.
作者
黎明
段茹茹
陈昊
谢惠华
LI Ming;DUAN Ruru;CHEN Hao;XIE Huihua(School of Information Engineering,Nanchang Hangkong University,Nanchang 330063;Key Laboratory of Nondestructive Testing,Ministry of Education,Nanchang Hangkong University,Nanchang 330063)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第9期800-810,共11页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61866025,61772255,61440049)
江西省图像处理与模式识别重点实验室开放基金项目(No.ET201604246)
江西省研究生创新专项资金项目(No.YC2017-S327)
江西省优势科技创新团队计划项目(No.20152BCB24004)资助~~
关键词
高维多目标优化
环境选择
进化算法
Many-objective Optimization
Environmental Selection
Evolutionary Algorithm
