Effects of noisy multiobjective test functions applied to evolutionary optimization algorithms
Loading...
Author (Corporation)
Publication date
2020
Typ of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Journal of Advances in Information Technology
Special issue
DOI of the original publication
Link
Series
Series number
Volume
11
Issue / Number
3
Pages / Duration
128-134
Patent number
Publisher / Publishing institution
Engineering and Technology
Place of publication / Event location
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
In this paper we study the effects of noise in multiobjective optimization problems. We consider a test function, which may be affected by noise with different strength and frequency of occurrence. To simplify the analysis, the noise is applied to only one of the objective functions, i.e. one of the objective functions is affected by additional random influences. Three different evolutionary algorithms for multiobjective problems are analyzed in this way: the Covariance Matrix Adaption Evolution Strategy (CMA-ES), the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and the Particle Swarm Optimization (PSO). The results are presented and analyzed with respect to the resulting Pareto fronts and with respect to the distribution of variable values during the algorithm run. It can be observed that all three algorithms are basically able to derive suitable results. However, only PSO leads to a sparse Pareto front in case of noisy and non-noisy situations while CMA and NSGA-II perform similarly well. In some situations for NSGA-II and more clearly for CMA-ES specific patterns for the variable values (denoted as striae in this paper) can be observed which appear to be partly caused by the noise.
Keywords
Subject (DDC)
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
1798-2340
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
Ryter, R., Hanne, T., & Dornberger, R. (2020). Effects of noisy multiobjective test functions applied to evolutionary optimization algorithms. Journal of Advances in Information Technology, 11(3), 128–134. https://doi.org/10.12720/jait.11.3.128-134