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Comparison of Reference- and Hypervolume-Based MOEA on Solving Many-Objective Optimization Problems

  • Dani IrawanEmail author
  • Boris Naujoks
Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11411)

Abstract

Hypervolume-based algorithms are not widely used for solving many-objective optimization problems due to the bottleneck of hypervolume computation. Approximation methods can alleviate the problem and are discussed and tested in this work. Several MOEAs are considered, but after pre-experimental tests, only two variants of SMS-EMOA are considered further. These algorithms are compared to NSGA-III, a reference-based algorithm. The results show that SMS-EMOA with hypervolume approximation is viable for many-objective optimization problems and is faster in convergence towards the Pareto-front.

Keywords

Hypervolume approximation MOEA Reference vector Many-objective optimization 
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Notes

Acknowledgments

This work is funded by the European Commission’s H2020 programme through the UTOPIAE Marie Curie Innovative Training Network, H2020-MSCA-ITN-2016, under Grant Agreement No. 722734 as well as through the Twinning project SYNERGY under Grant Agreement No. 692286.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Data Science, Engineering and AnalyticsTH-Köln - University of Applied SciencesCologneGermany

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