Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization

被引:15
作者
Siegmund, Florian [1 ]
Ng, Amos H. C. [1 ]
Deb, Kalyanmoy [2 ]
机构
[1] Univ Skovde, Virtual Syst Res Ctr, Skovde, Sweden
[2] Michigan State Univ, Dept Elect & Comp Engn, E Lansing, MI 48824 USA
来源
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT I | 2015年 / 9018卷
关键词
Evolutionary multi-objective optimization; Guided search; Reference point; Dynamic resampling; Budget allocation; ALGORITHMS;
D O I
10.1007/978-3-319-15934-8_25
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In Guided Evolutionary Multi-objective Optimization the goal is to find a diverse, but locally focused non-dominated front in a decision maker's area of interest, as close as possible to the true Pareto-front. The optimization can focus its efforts towards the preferred area and achieve a better result [7,9,13,17]. The modeled and simulated systems are often stochastic and a common method to handle the objective noise is Resampling. The given preference information allows to define better resampling strategies which further improve the optimization result. In this paper, resampling strategies are proposed that base the sampling allocation on multiple factors, and thereby combine multiple resampling strategies proposed by the authors in [15]. These factors are, for example, the Pareto-rank of a solution and its distance to the decision maker's area of interest. The proposed hybrid Dynamic Resampling Strategy DR2 is evaluated on the Reference point-guided NSGA-II optimization algorithm (R-NSGA-II) [9].
引用
收藏
页码:366 / 380
页数:15
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