期刊
APPLIED SOFT COMPUTING
卷 90, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2020.106192
关键词
Evolutionary algorithm; Reference point; Decision maker; Multiobjective optimization; Dominance relationship
资金
- National Natural Science Foundation of China [61876164, 61673331, 61379062, 61772178]
- Education Department of Hunan Province, China [17A212]
- Science and Technology Plan Project of Hunan Province, China [2018TP1036, 2016TP1020]
- Provinces and Cities Joint Foundation Project, China [2017JJ4001]
While traditional Pareto-based evolutionary multi-objective optimization (EMO) algorithms have shown an excellent balance between convergence and diversity on a wide range of practical problems with two or three objectives in real applications, the decision maker (DM) is interested in a unique set of solutions rather than the whole population on Pareto optimal front (POF). In addition, Paretobased EMO algorithms have some shortcomings in dealing with many-objective problems because of insufficient selection pressure toward trade-off solutions. Due to the above, it is crucial to incorporate DM preference information into EMO and seek a representative subset of Pareto optimal solutions with an increase in the number of objectives. This paper proposes a new dominance relationship, called Ra-dominance, which can improve diversity among the Pareto-equivalent solutions increase the selection pressure in evolutionary process. It has the ability to guide the population toward areas more responsive to the needs of the DM according to a reference point and preference angle. We use the new dominance relationship in the NSGA-II algorithm, and the efficacy and usefulness of the modified procedure are assessed through two- to ten-objective problems. Experimental results show that the algorithm applying this new dominance relationship is highly competitive when compared with four state-of-the-art preference-based EMO methods. (C) 2020 Elsevier B.V. All rights reserved.
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