MECHBench: A Set of Black-Box Optimization Benchmarks originated from Structural Mechanics
- URL: http://arxiv.org/abs/2511.10821v1
- Date: Thu, 13 Nov 2025 21:43:59 GMT
- Title: MECHBench: A Set of Black-Box Optimization Benchmarks originated from Structural Mechanics
- Authors: Iván Olarte Rodríguez, Maria Laura Santoni, Fabian Duddeck, Carola Doerr, Thomas Bäck, Elena Raponi,
- Abstract summary: This paper presents a curated set of optimization benchmarks rooted in structural mechanics.<n>The benchmarks are derived from vehicle crashworthiness scenarios.<n>Within this paper, the reader will find descriptions of the physical context of each case, the corresponding optimization problem formulations, and clear guidelines on how to employ the suite.
- Score: 1.4757194159888734
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Benchmarking is essential for developing and evaluating black-box optimization algorithms, providing a structured means to analyze their search behavior. Its effectiveness relies on carefully selected problem sets used for evaluation. To date, most established benchmark suites for black-box optimization consist of abstract or synthetic problems that only partially capture the complexities of real-world engineering applications, thereby severely limiting the insights that can be gained for application-oriented optimization scenarios and reducing their practical impact. To close this gap, we propose a new benchmarking suite that addresses it by presenting a curated set of optimization benchmarks rooted in structural mechanics. The current implemented benchmarks are derived from vehicle crashworthiness scenarios, which inherently require the use of gradient-free algorithms due to the non-smooth, highly non-linear nature of the underlying models. Within this paper, the reader will find descriptions of the physical context of each case, the corresponding optimization problem formulations, and clear guidelines on how to employ the suite.
Related papers
- Optimization is Not Enough: Why Problem Formulation Deserves Equal Attention [1.6516446394328081]
Black-box optimization is increasingly used in engineering design problems where simulation-based evaluations are costly and gradients are unavailable.<n>We show that context-agnostic strategies consistently lead to suboptimal or non-physical designs.<n>We motivate the development of new black-box benchmarks that reward physically informed and context-aware optimization strategies.
arXiv Detail & Related papers (2026-02-05T09:15:19Z) - A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers [1.8133635942659796]
We present a standardized benchmark suite for the evaluation of continuous black-box optimization algorithms, based on data clustering problems.<n>Our benchmark set is open-source and integrated with the IOHprofiler benchmarking framework to encourage its use in future research.
arXiv Detail & Related papers (2025-05-14T09:16:19Z) - A Novel Unified Parametric Assumption for Nonconvex Optimization [53.943470475510196]
Non optimization is central to machine learning, but the general framework non convexity enables weak convergence guarantees too pessimistic compared to the other hand.<n>We introduce a novel unified assumption in non convex algorithms.
arXiv Detail & Related papers (2025-02-17T21:25:31Z) - Explainable Benchmarking for Iterative Optimization Heuristics [0.8192907805418583]
We introduce the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms.
We examine the impact of different algorithmic components and configurations, offering insights into their performance across diverse scenarios.
arXiv Detail & Related papers (2024-01-31T14:02:26Z) - Analyzing and Enhancing the Backward-Pass Convergence of Unrolled
Optimization [50.38518771642365]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form.
This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method.
A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
arXiv Detail & Related papers (2023-12-28T23:15:18Z) - Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime [59.27851754647913]
Predictive optimization is the precise modeling of many real-world applications, including energy cost-aware scheduling and budget allocation on advertising.
We develop a modular framework to benchmark 11 existing PtO/PnO methods on 8 problems, including a new industrial dataset for advertising.
Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
arXiv Detail & Related papers (2023-11-13T13:19:34Z) - Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver.
This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
arXiv Detail & Related papers (2023-01-28T01:50:42Z) - Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
We present the first efficient scalable and general framework that can directly search on the tasks of interest.
Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree.
We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
arXiv Detail & Related papers (2022-09-27T17:51:31Z) - Tree ensemble kernels for Bayesian optimization with known constraints
over mixed-feature spaces [54.58348769621782]
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search.
Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function.
Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
arXiv Detail & Related papers (2022-07-02T16:59:37Z) - A unified surrogate-based scheme for black-box and preference-based
optimization [2.561649173827544]
We show that black-box and preference-based optimization problems are closely related and can be solved using the same family of approaches.
We propose the generalized Metric Response Surface (gMRS) algorithm, an optimization scheme that is a generalization of the popular MSRS framework.
arXiv Detail & Related papers (2022-02-03T08:47:54Z) - On the Optimality of Batch Policy Optimization Algorithms [106.89498352537682]
Batch policy optimization considers leveraging existing data for policy construction before interacting with an environment.
We show that any confidence-adjusted index algorithm is minimax optimal, whether it be optimistic, pessimistic or neutral.
We introduce a new weighted-minimax criterion that considers the inherent difficulty of optimal value prediction.
arXiv Detail & Related papers (2021-04-06T05:23:20Z) - Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and
Open Challenges [0.0]
Research on new optimization algorithms is often funded based on the motivation that such algorithms might improve the capabilities to deal with real-world and industrially relevant challenges.
A large number of test problems and benchmark suites have been developed and used for comparative assessments of algorithms.
arXiv Detail & Related papers (2020-07-01T15:09:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.