GP-DEMO: Differential Evolution for Multiobjective Optimization based on Gaussian Process models

•New surrogate-model-based evolutionary multiobjective algorithm GP-DEMO is proposed.•New relations for comparing solutions under uncertainty are defined.•Comparing solutions under uncertainty prevents wrongly performed comparisons.•GP-DEMO in comparison to DEMO obtained similar results with less ex...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research Vol. 243; no. 2; pp. 347 - 361
Main Authors: Mlakar, Miha, Petelin, Dejan, Tušar, Tea, Filipič, Bogdan
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2015
Elsevier Sequoia S.A
Subjects:
Algorithms
Evolutionary algorithms
Gaussian Process modeling
Genetic algorithms
Mathematical models
Mathematical problems
Multiple objective programming
Normal distribution
Operations research
Optimization
Optimization algorithms
Probable Pareto dominance
Studies
Surrogate models
Evolutionary algorithms
Surrogate models
Gaussian Process modeling
Probable Pareto dominance
Multiple objective programming
ISSN:0377-2217, 1872-6860
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•New surrogate-model-based evolutionary multiobjective algorithm GP-DEMO is proposed.•New relations for comparing solutions under uncertainty are defined.•Comparing solutions under uncertainty prevents wrongly performed comparisons.•GP-DEMO in comparison to DEMO obtained similar results with less exact evaluations. This paper proposes a novel surrogate-model-based multiobjective evolutionary algorithm called Differential Evolution for Multiobjective Optimization based on Gaussian Process models (GP-DEMO). The algorithm is based on the newly defined relations for comparing solutions under uncertainty. These relations minimize the possibility of wrongly performed comparisons of solutions due to inaccurate surrogate model approximations. The GP-DEMO algorithm was tested on several benchmark problems and two computationally expensive real-world problems. To be able to assess the results we compared them with another surrogate-model-based algorithm called Generational Evolution Control (GEC) and with the Differential Evolution for Multiobjective Optimization (DEMO). The quality of the results obtained with GP-DEMO was similar to the results obtained with DEMO, but with significantly fewer exactly evaluated solutions during the optimization process. The quality of the results obtained with GEC was lower compared to the quality gained with GP-DEMO and DEMO, mainly due to wrongly performed comparisons of the inaccurately approximated solutions.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2014.04.011