Faster Hypervolume-Based Search Using Monte Carlo Sampling

被引：87

作者：

Bader, Johannes ^{[1
]}

Deb, Kalyanmoy ^{[1
]}

Zitzler, Eckart ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Comp Engn & Networks Lab, CH-8092 Zurich, Switzerland

来源：

MULTIPLE CRITERIA DECISION MAKING FOR SUSTAINABLE ENERGY AND TRANSPORTATION SYSTEMS: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON MULTIPLE CRITERIA DECISION MAKING | 2010年 / 634卷

关键词：

Hypervolume indicator; Monte Carlo sampling; Evolutionary multiobjective algorithms; ALGORITHM; SELECTION; OPTIMIZATION;

D O I：

10.1007/978-3-642-04045-0_27

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In recent years, the hypervolume indicator - a set quality measure considering the dominated portion of the objective space - has gained increasing attention in the context of multiobjective search. This is mainly due to the following feature: whenever one Pareto set approximation completely dominates another approximation, the hypervolume of the former will be greater than the hypervolume of the latter. Unfortunately, the calculation of the hypervolume measure is computationally highly demanding, and current algorithms are exponential in the number of objectives. This paper proposes a methodology based on Monte Carlo sampling to estimate the hypervolume contribution of single solutions regarding a specific Pareto set approximation. It is therefore designed to be used in the environmental selection process of an evolutionary algorithm, and allows substantial speedups in hypervolume-based search as the experimental results demonstrate.

引用

页码：313 / 326

页数：14

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