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Effects of Noisy Multiobjective Test Functions Applied to Evolutionary Optimization Algorithms

Remo Ryter, Thomas Hanne, and Rolf Dornberger
Institute for Information Systems, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland

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.
Index Terms—multiobjective optimization, evolutionary algorithms, noise, evolutionary multiobjective optimization, robust optimization
Cite: Remo Ryter, Thomas Hanne, and Rolf Dornberger, "Effects of Noisy Multiobjective Test Functions Applied to Evolutionary Optimization Algorithms," Journal of Advances in Information Technology, Vol. 11, No. 3, pp. 128-134, August 2020. doi: 10.12720/jait.11.3.128-134
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.