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.

翻译：随着优化挑战的不断发展，我们的工具和理解也必须随之进步。为有效评估、验证和比较优化算法，使用包含具有不同特征的各种问题实例的基准测试套件至关重要。传统的基准测试套件通常包含大量固定的测试函数，这使得将这些函数与特定的研究目标（例如在可控条件下系统评估算法）对齐变得困难。本文提出了面向单目标、边界约束、连续数值优化的通用数值基准生成器（GNBG）。与依赖多种基线函数和变换的现有方法不同，GNBG使用单一、参数化且可配置的基线函数。这种设计允许对多种问题特征进行可控调节。使用GNBG的研究人员可以生成涵盖广泛形态特征的问题实例，包括从单峰到高度多峰函数、各种局部最优模式以及从对称到高度非对称的结构。生成的问题还可在可分离性、变量交互结构、维度、条件数和盆地形状方面有所变化。这些可定制特征使得优化算法的系统评估和比较成为可能，从而允许研究人员在多样且可控的条件下探究其优势与不足。