期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 25, 期 1, 页码 21-34出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2020.2991040
关键词
Grid system; inverted generational distance (IGD); many-objective optimization; performance indicator
资金
- National Natural Science Foundation of China [61300159, 61732006, 61876075, 61572127, 61872077, 61832004]
- Natural Science Foundation of Jiangsu Province of China [BK20181288]
- China Postdoctoral Science Foundation [2015M571751]
- Aeronautical Science Foundation of China [20175552042]
Grid-IGD is proposed as a performance indicator for evaluating PF approximations in multi/many-objective optimization, estimating both convergence and diversity with lower time complexity, and possessing desirable properties like Pareto compliance and immunity to dominated/duplicate solutions.
Assessing the performance of Pareto front (PF) approximations is a key issue in the field of evolutionary multi/many-objective optimization. Inverted generational distance (IGD) has been widely accepted as a performance indicator for evaluating the comprehensive quality for a PF approximation. However, IGD usually becomes infeasible when facing a real-world optimization problem as it needs to know the true PF a priori. In addition, the time complexity of IGD grows quadratically with the size of the solution/reference set. To address the aforementioned issues, a grid-based IGD (Grid-IGD) is proposed to estimate both convergence and diversity of PF approximations for multi/many-objective optimization. In Grid-IGD, a set of reference points is generated by estimating PFs of the problem in question, based on the representative nondominated solutions of all the approximations in a grid environment. To reduce the time complexity, Grid-IGD only considers the closest solution within the grid neighborhood in the approximation for every reference point. Grid-IGD also possesses other desirable properties, such as Pareto compliance, immunity to dominated/duplicate solutions, and no need of normalization. In the experimental studies, Grid-IGD is verified on both the artificial and real PF approximations obtained by five many-objective optimizers. Effects of the grid specification on the behavior of Grid-IGD are also discussed in detail theoretically and experimentally.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据