🤖 AI Summary
Conventional benchmark suites employ fixed, inflexible functions, limiting systematic and goal-oriented evaluation of optimization algorithms. Method: This paper proposes a通用可配置的 continuous numerical optimization benchmark generator, introducing a novel paradigm based on a single parameterized basis function—replacing traditional multi-function composition. Through parameterized modeling, high-dimensional nonlinear transformations, structured perturbations, and configurable feature mapping, the generator enables full-dimensional, fine-grained, and reproducible control over critical morphological properties—including modality, symmetry, separability, condition number, and basin geometry. Contribution/Results: Generated instances comprehensively span the spectrum of unimodal/strongly multimodal, low-/high-dimensional, and well-/ill-conditioned scenarios. The framework significantly enhances the systematicity, controllability, and interpretability of algorithmic evaluation in continuous optimization.
📝 Abstract
As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives, such as the systematic evaluation of algorithms under controllable conditions. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for single-objective, box-constrained, continuous numerical optimization. Unlike existing approaches that rely on multiple baseline functions and transformations, GNBG utilizes a single, parametric, and configurable baseline function. This design allows for control over various problem characteristics. Researchers using GNBG can generate instances that cover a broad array of morphological features, from unimodal to highly multimodal functions, various local optima patterns, and symmetric to highly asymmetric structures. The generated problems can also vary in separability, variable interaction structures, dimensionality, conditioning, and basin shapes. These customizable features enable the systematic evaluation and comparison of optimization algorithms, allowing researchers to probe their strengths and weaknesses under diverse and controllable conditions.
