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A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms.

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Summary

This study systematically evaluated multi-objective evolutionary algorithms (MOEAs), finding that tuned configurations outperform default settings. Indicator-based MOEAs show surprising competitiveness for many-objective problems.

Keywords:
Multi-objective optimizationautomatic algorithm configuration.evolutionary algorithmsperformance assessment

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Computer Science

Background:

  • Multi-objective evolutionary algorithms (MOEAs) have a rich history, but comparisons are often inconsistent.
  • Lack of standardized baselines and experimental setups leads to a disputed state-of-the-art in MOEA research.
  • Previous studies often compare new MOEAs against outdated algorithms with non-optimized parameters.

Purpose of the Study:

  • To conduct a systematic and comprehensive evaluation of a wide range of MOEAs.
  • To establish a common, reproducible baseline for MOEA performance comparison.
  • To investigate the impact of parameter tuning on MOEA performance across diverse scenarios.

Main Methods:

  • Separated higher-level MOEA components from underlying, tunable parameters.
  • Tuned parameters for each MOEA and scenario using automatic algorithm configuration.
  • Evaluated MOEAs across a broad spectrum of experimental scenarios.
  • Analyzed problem-specific features, metric agreement, and tuned vs. default configurations.

Main Results:

  • Confirmed existing knowledge while revealing new insights into MOEA performance, particularly for many-objective problems.
  • Demonstrated that indicator-based MOEAs can be highly competitive under specific conditions.
  • Showcased significant performance improvements achieved through automatic parameter tuning.
  • Highlighted discrepancies between default and optimized configurations.

Conclusions:

  • Systematic evaluation with tuned parameters provides a more accurate assessment of MOEA capabilities.
  • The competitiveness of indicator-based MOEAs for many-objective problems warrants further investigation.
  • Publicly available data will facilitate future research and establish a robust baseline for MOEA comparisons.