mlsanityData to 14 Oct 2026

A Literature Survey of Benchmark Functions For Global Optimization Problems

arXiv 1308.4008 ↗First posted 19 Aug 2013cs.AI math.OCcited by 77

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Abstract (arXiv)

Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.

Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Aug 2013.

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Citing papers per year: 2021: 8; 2022: 12; 2023: 18; 2024: 10; 2025: 15; 2026: 14.

77 papers.

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  1. 2021LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lassocited by 41
  2. 2021Bayesian Optimization of Function Networkscited by 33
  3. 2021Transfer Learning with Gaussian Processes for Bayesian Optimizationcited by 26
  4. 2022Symmetry Teleportation for Accelerated Optimizationcited by 25
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  6. 2022TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learningcited by 15
  7. 2025Offline Model-Based Optimization: Comprehensive Reviewcited by 15
  8. 2022Meta-Learning Priors for Safe Bayesian Optimizationcited by 14
  9. 2022SP2: A Second Order Stochastic Polyak Methodcited by 13
  10. 2022Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimizationcited by 10
  11. 2023Bayesian Optimization of Function Networks with Partial Evaluationscited by 10
  12. 2023On the role of Model Uncertainties in Bayesian Optimizationcited by 8
38 more
  1. 2023Quantum Hamiltonian Descentcited by 7
  2. 2023Quantum Langevin Dynamics for Optimizationcited by 7
  3. 2024Bandits with Preference Feedback: A Stackelberg Game Perspectivecited by 7
  4. 2023No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximationscited by 6
  5. 2023Improving Energy Conserving Descent for Machine Learning: Theory and Practicecited by 4
  6. 2023Optimizing Algorithms From Pairwise User Preferencescited by 4
  7. 2022GLISp-r: A preference-based optimization algorithm with convergence guaranteescited by 3
  8. 2023Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimizationcited by 3
  9. 2023Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descentcited by 3
  10. 2025Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?cited by 3
  11. 2025Bayesian Optimization from Human Feedback: Near-Optimal Regret Boundscited by 3
  12. 2026Evolutionary Multi-Task Optimization for LLM-Guided Program Discoverycited by 3
  13. 2022Hierarchical Collaborative Hyper-parameter Tuningcited by 2
  14. 2023LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regionscited by 2
  15. 2024Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programmingcited by 2
  16. 2024BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Searchcited by 2
  17. 2025Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimizationcited by 2
  18. 2021Optimizing over an ensemble of neural networkscited by 1
  19. 2024Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methodscited by 1
  20. 2024Surrogate-guided optimization in quantum networkscited by 1
  21. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  22. 2024Alpha Entropy Search for New Information-based Bayesian Optimizationcited by 1
  23. 2025Parameter Tuning of the Firefly Algorithm by Three Tuning Methods: Standard Monte Carlo, Quasi-Monte Carlo and Latin Hypercube Sampling Methodscited by 1
  24. 2025The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metriccited by 1
  25. 2021Making Differentiable Architecture Search less localcited by 0
  26. 2021Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus diseasecited by 0
  27. 2021A Granular Sieving Algorithm for Deterministic Global Optimizationcited by 0
  28. 2021Dynamic communication topologies for distributed heuristics in energy system optimization algorithmscited by 0
  29. 2022Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific modelscited by 0
  30. 2022A unified surrogate-based scheme for black-box and preference-based optimizationcited by 0
  31. 2022An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constantscited by 0
  32. 2022Asymptotic Optimality of Myopic Ranking and Selection Procedurescited by 0
  33. 2023Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogatescited by 0
  34. 2023Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimizationcited by 0
  35. 2023Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levelscited by 0
  36. 2023Learning and Generalizing Polynomials in Simulation Metamodelingcited by 0
  37. 2023Performance Comparison of Design Optimization and Deep Learning-based Inverse Designcited by 0
  38. 2023Ten New Benchmarks for Optimizationcited by 0

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