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February 8, 2026ACM Transactions on Evolutionary Learning and Optimization

BONO-Bench: A Comprehensive Test Suite for Bi-objective Numerical Optimization with Traceable Pareto Sets

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Authors

LSLennart SchäpermeierPKPascal Kerschke

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Overview

Proposes an extensive test suite for bi-objective optimization, highlighting realistic problem generation and performance indicators.

Key Points

  • The research aims to create a test suite for bi-objective numerical optimization that overcomes limitations of current benchmarking methods.
  • Develop a problem generation approach using convex-quadratic functions.
  • Create multiple problem categories with varying properties.
  • Implement a public Python package for reproducibility.
  • Introduced BONO-Bench with 20 distinct problem categories.
  • Demonstrated the generator's ability to configure properties like decision variables and optimization landscapes.
  • Facilitated precise approximation of optimal Pareto fronts.

Cite This Study

Schäpermeier et al. (2026) studied this question.

synapsesocial.com/papers/698829f20fc35cd7a88499efhttps://doi.org/10.1145/3795775
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