Representation of the pareto front for heterogeneous multi-objective optimization

被引:0
作者
Thomann J. [1 ]
Eichfelder G. [1 ]
机构
[1] Institute for Mathematics, Technische Universität Ilmenau, Po 10 05 65, Ilmenau
来源
Journal of Applied and Numerical Optimization | 2019年 / 1卷 / 03期
关键词
Heterogeneous optimization; Multi-objective optimization; Tammer-Weidner-functional; Trust region algorithm;
D O I
10.23952/jano.1.2019.3.08
中图分类号
学科分类号
摘要
Optimization problems with multiple objectives which are expensive, i. e., where function evaluations are time consuming, are difficult to solve. Finding at least one locally optimal solution is already a difficult task. In case only one of the objective functions is expensive while the others are cheap, for instance, analytically given, this can be used in the optimization procedure. Using a trust-region approach and the Tammer-Weidner-functional for finding descent directions, in [19] an algorithm was proposed which makes use of the heterogeneity of the objective functions. In this paper, we present three heuristic approaches, which allow to find additional optimal solutions of the multiobjective optimization problem and by that representations at least of parts of the Pareto front. We present the related theoretical results as well as numerical results on some test instances. © 2019 Journal of Applied and Numerical Optimization.
引用
收藏
页码:293 / 323
页数:30
相关论文
共 20 条
[1]  
Bouza G., Quintana E., Tammer C., A unified characterization of nonlinear scalarizing functionals in optimization, Vietnam J. Math, 47, pp. 683-713, (2019)
[2]  
Conn A., Gould N., Toint P., Trust-Region Methods, MPS-SIAM Series on Optimization, (2000)
[3]  
Conn A., Scheinberg K., Vicente L., Introduction to Derivative-Free Optimization, MPS-SIAM Series on Optimization, (2009)
[4]  
Drummond L., Svaiter B. F., A steepest descent method for vector optimization, J. Comput. Appl. Math, 175, pp. 395-414, (2005)
[5]  
Eichfelder G., Scalarizations for adaptively solving multi-objective optimization problems, Comput. Optim. Appl, 44, pp. 249-273, (2009)
[6]  
Eichfelder G., Adaptive Scalarization Methods in Multiobjective Optimization, (2008)
[7]  
Eichfelder G., Jahn J., Vector optimization problems and their solution concepts, Recent Developments in Vector Optimization, pp. 1-27, (2012)
[8]  
Eichfelder G., Pilecka M., Ordering Structures and Their Applications, Applications of Nonlinear Analysis, pp. 256-304, (2018)
[9]  
Fliege J., Drummond L. G., Svaiter B., Newton‘s method for multiobjective optimization, SIAM J. Optim, 20, pp. 602-626, (2009)
[10]  
Fliege J., Svaiter B. F., Steepest descent methods for multicriteria optimization, Math. Method. Oper. Res, 51, pp. 479-494, (2000)