GNBG-C: A Unified and Configurable Benchmark Generator for Constrained Continuous Optimization
Abstract
Many real-world optimization tasks are governed not only by what should be optimized, but also by what is allowed: a solution must first satisfy physical, operational, safety, budgetary, or structural requirements before its objective value becomes meaningful. Yet benchmark design for constrained continuous optimization often treats these restrictions as fixed properties of predefined test instances. Existing generators and benchmark suites provide essential foundations for reproducible comparison, but they either focus primarily on configurable objective landscapes or expose constraint properties within limited, predefined regimes, making it difficult to ask targeted questions about constraint-induced difficulty: whether an optimizer struggles because feasible regions are small, boundaries are restrictive, variables are tightly coupled, or constraint information is poorly scaled. We introduce GNBG-C, a Generalized Numerical Benchmark Generator for Constrained Continuous Optimization. The central idea is to treat the feasible region itself as a configurable object rather than as an inseparable attachment to a fixed test function. By generating controlled constraint regimes around the known GNBG global minimizer, the generator enables systematic studies in which feasibility pressure can be varied while preserving that point as a global minimizer of the constrained problem. This allows researchers to examine how optimizers discover feasibility, handle constraint pressure, and improve objective quality under diverse and controllable feasibility conditions. GNBG-C therefore provides a flexible and auditable framework for probing the strengths and weaknesses of constrained continuous optimizers under targeted forms of feasibility-side difficulty.
Related
- A Stochastic Proximal-Gradient Method for Regularized Generally Constrained Continuous Optimization
- Robust Chance-Constrained Optimization using a Continuous Parameter Space Wasserstein-2 Ambiguity Set of Gaussian Mixtures
- Reinforcement learning for constrained stochastic linear--quadratic control in continuous time
- Optimal Control of State Constrained Systems via Measure Relaxations and Polynomial Optimization
- Continuous-Time Constrained Linear Quadratic Regulator for Switched Linear Systems
BibTeX
@misc{ghasemzadeh2026gnbgc,
title = {{GNBG-C: A Unified and Configurable Benchmark Generator for Constrained Continuous Optimization}},
author = {Ali Ghasemzadeh and Erfan Babakhani and Armin Khosravi and Pariya Ghasemzadeh and Yong-Hong Kuo and Seyedali Mirjalili and Amir H. Gandomi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37590},
eprint = {2610.37590},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37590},
}
RIS
TY - UNPB
TI - GNBG-C: A Unified and Configurable Benchmark Generator for Constrained Continuous Optimization
AU - Ali Ghasemzadeh
AU - Erfan Babakhani
AU - Armin Khosravi
AU - Pariya Ghasemzadeh
AU - Yong-Hong Kuo
AU - Seyedali Mirjalili
AU - Amir H. Gandomi
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37590
UR - https://arxiv.org/abs/2610.37590
AB - Many real-world optimization tasks are governed not only by what should be optimized, but also by what is allowed: a solution must first satisfy physical, operational, safety, budgetary, or structural requirements before its objective value becomes meaningful. Yet benchmark design for constrained continuous optimization often treats these restrictions as fixed properties of predefined test instances. Existing generators and benchmark suites provide essential foundations for reproducible comparison, but they either focus primarily on configurable objective landscapes or expose constraint properties within limited, predefined regimes, making it difficult to ask targeted questions about constraint-induced difficulty: whether an optimizer struggles because feasible regions are small, boundaries are restrictive, variables are tightly coupled, or constraint information is poorly scaled. We introduce GNBG-C, a Generalized Numerical Benchmark Generator for Constrained Continuous Optimization. The central idea is to treat the feasible region itself as a configurable object rather than as an inseparable attachment to a fixed test function. By generating controlled constraint regimes around the known GNBG global minimizer, the generator enables systematic studies in which feasibility pressure can be varied while preserving that point as a global minimizer of the constrained problem. This allows researchers to examine how optimizers discover feasibility, handle constraint pressure, and improve objective quality under diverse and controllable feasibility conditions. GNBG-C therefore provides a flexible and auditable framework for probing the strengths and weaknesses of constrained continuous optimizers under targeted forms of feasibility-side difficulty.
ER -