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

页码：741 / +

页数：2

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