Best practices for comparing optimization algorithms
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Abstract (arXiv)
Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking process of optimization algorithms, and discuss the challenges of fair comparison. We provide suggestions for each step of the comparison process and highlight the pitfalls to avoid when evaluating the performance of optimization algorithms. We also discuss various methods of reporting the benchmarking results. Finally, some suggestions for future research are presented to improve the current benchmarking process.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Sep 2017.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 0; 2025: 4; 2026: 1.
10 papers.
- 2022A Comparative Study of Graph Matching Algorithms in Computer Visioncited by 14
- 2022Transformer-Based Learned Optimizationcited by 13
- 2022Optimization without Backpropagationcited by 11
- 2022A Framework for Generating Informative Benchmark Instancescited by 4
- 2025The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrongcited by 3
- 2025A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: Avoiding Unreliable Conclusionscited by 1
- 2021A Framework for Discovering Optimal Solutions in Photonic Inverse Designcited by 0
- 2025A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecturecited by 0
- 2025L-SR1: Learned Symmetric-Rank-One Preconditioningcited by 0
- 2026Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolutioncited by 0
- 2026Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution31 Jul 2026
- 2025L-SR1: Learned Symmetric-Rank-One Preconditioning17 Aug 2025
- 2025A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture17 Jun 2025
- 2025The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong12 May 2025
- 2025A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: Avoiding Unreliable Conclusions20 Mar 2025
- 2022Transformer-Based Learned Optimization2 Dec 2022
- 2022Optimization without Backpropagation13 Sep 2022
- 2022A Comparative Study of Graph Matching Algorithms in Computer Vision1 Jul 2022
- 2022A Framework for Generating Informative Benchmark Instances29 May 2022
- 2021A Framework for Discovering Optimal Solutions in Photonic Inverse Design3 Jun 2021