A Generalized and Configurable Benchmark Generator for Continuous Unconstrained Numerical Optimization

TL;DR

This paper presents GNBG, a flexible benchmark generator for continuous optimization that creates diverse, controllable test problems to better evaluate and compare optimization algorithms.

Contribution

The paper introduces GNBG, a novel, configurable benchmark generator that produces a wide variety of problem instances with controllable features for continuous optimization.

Findings

GNBG can generate diverse problem instances with controllable features.

It enables systematic evaluation of optimization algorithms under various conditions.

The generator supports a broad range of problem characteristics, enhancing benchmarking flexibility.

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 singleobjective, box-constrained, continuous numerical optimization. Unlike the commonly used test suites 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 range 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 methods, allowing researchers to examine the strengths and weaknesses of algorithms under diverse and controllable conditions.