4.6 Review

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

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

  1. EPSRC [EP/M017869/1]
  2. Joint Research Fund for Overseas Chinese, Hong Kong
  3. Macao Scholars of the National Natural Science Foundation of China [61428302]
  4. Honda Research Institute Europe
  5. Engineering and Physical Sciences Research Council [EP/M017869/1] Funding Source: researchfish
  6. EPSRC [EP/M017869/1] Funding Source: UKRI

向作者/读者索取更多资源

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.

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