Decomposing the user-preference in multiobjective optimization

Decomposing the user-preference in multiobjective optimization
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多目标优化中分解用户偏好

DOI:
10.1007/s00500-015-1736-z
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发表时间:
2015-07
影响因子:
8.7
通讯作者:
Miqing Li
Miqing Li
中科院分区:
计算机科学2区
文献类型:
--
作者:
Guo Yu;Jinhua Zheng;Ruimin Shen;Miqing Li

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决策者(DM)的偏好信息(例如参考点)经常用于多目标优化;然而,指定参考点的位置对多目标进化算法的性能有不利影响。
Preference information (such as the reference point) of the decision maker (DM) is often used in multiobjective optimization; however, the location of the specified reference point has a detrimental effect on the performance of multiobjective evolutionary algorithms (MOEAs). Inspired by multiobjective evolutionary algorithm-based decomposition (MOEA/D), this paper proposes an MOEA to decompose the preference information of the reference point specified by the DM into a number of scalar optimization subproblems and deals with them simultaneously (called MOEA/D-PRE). This paper presents an approach of iterative weight to map the desired region of the DM, which makes the algorithm easily obtain the desired region. Experimental results have demonstrated that the proposed algorithm outperforms two popular preference-based approaches,g-dominance andr-dominance, on continuous multiobjective optimization problems (MOPs), especially on many-objective optimization problems. Moreover, this study develops distinct models to satisfy different needs of the DM, thus providing a new way to deal with preference-based multiobjective optimization. Additionally, in terms of the shortcoming of MOEA/D-PRE, an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems.
DOI: 10.1364/jocn.3.000178
发表时间: 2011-03
期刊: IEEE/OSA Journal of Optical Communications and Networking
影响因子: --
作者:
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期刊: --
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发表时间: 2011-02
期刊: Natural Computing
影响因子: 2.1
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