期刊
COMPLEX & INTELLIGENT SYSTEMS
卷 3, 期 4, 页码 233-245出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s40747-017-0053-9
关键词
Multi-objective optimization; Preference modeling; Preference learning
资金
- EPSRC [EP/M017869/1]
- Joint Research Fund for Overseas Chinese, Hong Kong
- Macao Scholars of the National Natural Science Foundation of China [61428302]
- Honda Research Institute Europe
- Engineering and Physical Sciences Research Council [EP/M017869/1] Funding Source: researchfish
- EPSRC [EP/M017869/1] Funding Source: UKRI
Evolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over the past decade, preference-based multi-objective optimization has attracted increasing attention from both academia and industry due to its significance in both theory and practice. Significant progress has been made in evolutionary multi-objective optimization and multi-criteria decision communities, although many open issues still remain to be addressed. This paper provides a concise review on preference-based multi-objective optimization, including various preference modeling methods and existing preference-based optimization methods, as well as a brief discussion of the main future challenges.
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