A Scalable Benchmark Framework for Bilevel Optimization Without Variable Separability

Benchmark test suites play a critical role in the evaluation of evolutionary algorithms. In bilevel single-objective optimization, however, benchmarks tailored to evolutionary computation remain limited. The widely used SMD test suite exhibits several shortcomings, including performance saturation, insufficient coverage of non-separable problems, and limited representational coverage. To address these issues, this paper proposes a new benchmark construction methodology that extends the original SMD design philosophy while alleviating its inherent limitations. The proposed approach aims to mitigate overfitting caused by performance saturation, enables scalable problem construction without relying on variable separability, and preserves the core characteristics of bilevel benchmarks while supporting the inclusion of new problem types that better reflect practical scenarios. Furthermore, a minimal implementation of the proposed methodology is provided, and its feasibility is demonstrated through empirical studies using several bilevel optimization algorithms. The source code is publicly available at https://gitee.com/jingyp/blopbench