Mathematical and Computational Applications (Nov 2022)

Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?

  • Antonio J. Nebro,
  • Jesús Galeano-Brajones,
  • Francisco Luna,
  • Carlos A. Coello Coello

DOI
https://doi.org/10.3390/mca27060103
Journal volume & issue
Vol. 27, no. 6
p. 103

Abstract

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NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving multi-objective optimization problems. However, its most common usage, particularly when dealing with continuous problems, is circumscribed to a standard algorithmic configuration similar to the one described in its seminal paper. In this work, our aim is to show that the performance of NSGA-II, when properly configured, can be significantly improved in the context of large-scale optimization. It leverages a combination of tools for automated algorithmic tuning called irace, and a highly configurable version of NSGA-II available in the jMetal framework. Two scenarios are devised: first, by solving the Zitzler–Deb–Thiele (ZDT) test problems, and second, when dealing with a binary real-world problem of the telecommunications domain. Our experiments reveal that an auto-configured version of NSGA-II can properly address test problems ZDT1 and ZDT2 with up to 217=131,072 decision variables. The same methodology, when applied to the telecommunications problem, shows that significant improvements can be obtained with respect to the original NSGA-II algorithm when solving problems with thousands of bits.

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