Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms

被引：0
|
作者：
Ishibuchi, Hisao [1 ]
Nojima, Yusuke [1 ]
Narukawa, Kaname [1 ]
Doi, Tsutomu [1 ]
机构：
[1] Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Naka Ku, 1-1 Gakuen Cho, Sakai, Osaka 5998531, Japan
来源：
GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 | 2006年
基金：
日本学术振兴会;
关键词：
evolutionary multiobjective optimization (EMO); many-objective optimization; multiobjective combinatorial optimization; decision maker's preference; balance between convergence and diversity;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The main characteristic feature of evolutionary multiobjective optimization (EMO) is that no a priori information about the decision maker's preference is utilized in the search phase. EMO algorithms try to find a set of well-distributed Pareto-optimal solutions with a wide range of objective values. It is, however, very difficult for EMO algorithms to find a good solution set of a multiobjective combinatorial optimization problem with many decision variables and/or many objectives. In this paper, we propose an idea of incorporating the decision maker's preference into EMO algorithms to efficiently search for Pareto-optimal solutions of such a hard multiobjective optimization problem.
引用
收藏
页码：741 / +
页数：2
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