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基于类圆映射的高维多目标可视化方法
The Quasi-Circular Mapping Visualization for Many-Objective

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黎明 1,2   黄珊 1   陈昊 1,2 *   李军华 1,2  
文摘 可视化技术有利于对高维多目标优化问题求解所得的解集进行评价与分析,但是现有的高维多目标可视化方法无法有效保持解集的Pareto支配关系、前沿密度分布及形状。针对以上问题,本文提出类圆映射可视化方法.首先将多目标按相关性均匀排列在单位圆圆弧上,根据适应度函数值将解集映射为类圆空间内的一个多边形,并通过多边形的几何中心和面积对解集进行3维可视化.在此基础上对类圆支配与均衡性进行了定义,并对类圆映射下的支配关系、映射遮挡等进行了理论分析与证明.与平行坐标系、主成分分析方法和径向可视化方法相比表明,本文方法能保持解集Pareto支配关系,并能反映解集在原始空间的密度分布和形状。此外,还能有效避免解集映射点遮档.其有利于决策者进行可视化评价和选择高维多目标解集.
其他语种文摘 Visualization technology is conducive to the evaluation and analysis of the solution sets obtained by solving many-objective optimization problem,but the existing many-objective visualization technology cannot effectively preserve Pareto dominance relation,maintain frontier distribution and retain shape. To solve the above problems, this paper presents quasi-circular mapping visualization. Many-objective are uniformly distributed in order on a unit arc according to their correlation. Based on the fitness function value, the solution sets are mapped into a polygon in quasi-circular space. So 3 dimensional visualization of the solution set is achieved through the geometric center and area of polygons. On the basis of this, the quasi-circular domination and equilibrium are defined. The dominance relation and mapping occlusion under quasi-circular mapping are theoretically analyzed and proved. Compared with parallel coordinates,principal component analysis and radial visualization, this method can preserve the Pareto dominance. In addition, it can also reflect frontier distribution and shape in the original space and effectively avoid data blocking. It helps decision makers to evaluate and select many-objective solution sets visually.
来源 电子学报 ,2019,47(6):1185-1193 【核心库】
DOI 10.3969/j.issn.0372-2112.2019.06.001
关键词 多目标优化问题 ; 可视化技术 ; 高维多目标可视化 ; 类圆映射 ; Pareto支配 ; Pareto前沿形状
地址

1. 南昌航空大学信息工程学院, 江西, 南昌, 330063  

2. 南昌航空大学, 江西省图像处理与模式识别重点实验室, 江西, 南昌, 330063

语种 中文
文献类型 研究性论文
ISSN 0372-2112
学科 自动化技术、计算机技术
基金 国家自然科学基金 ;  江西省创新驱动“5511”工程优势学科创新团队 ;  江西省科技厅项目 ;  江西省研究生创新专项基金
文献收藏号 CSCD:6668542

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引证文献 2

1 韩红桂 自适应分解式多目标粒子群优化算法 电子学报,2020,48(7):1245-1254
CSCD被引 5

2 戴永彬 基于类圆映射的多目标粒子群优化算法 计算机应用研究,2021,38(12):3673-3677
CSCD被引 0 次

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