Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES

被引:20
|
作者
Arnold, Dirk V. [1 ]
Porter, Jeremy [1 ]
机构
[1] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 4R2, Canada
来源
GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2015年
关键词
Stochastic black-box optimization; constraint handling; penalty method; augmented Lagrangian; evolution strategy; BEHAVIOR;
D O I
10.1145/2739480.2754813
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We consider the problem of devising an approach for handling inequality constraints in evolution strategies that allows converging linearly to optimal solutions on sphere functions with a single linear constraint. An analysis of the single-step behaviour of the (1 + 1)-ES shows that the task of balancing improvements in the objective with those in the constraint function is quite delicate, and that adaptive approaches need to be carefully designed in order to avoid failure. Based on the understanding gained, we propose a simple augmented Lagrangian approach and experimentally demonstrate good performance on a broad range of sphere functions as well as on moderately ill-conditioned ellipsoids with a single linear constraint.
引用
收藏
页码:249 / 256
页数:8
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