The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
A literature survey of benchmark functions for global optimisation problems
Open accessAbstract
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.
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Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Faires, J. D. (1978) · Cited by 6,711
Powell, M. J. D. (1964) · The Computer Journal · doi:10.1093/comjnl/7.2.155 · Cited by 4,632
A Rapidly Convergent Descent Method for Minimization
Fletcher, R., & Powell, M. J. D. (1963) · The Computer Journal · doi:10.1093/comjnl/6.2.163 · Cited by 4,583
Conditioning of quasi-Newton methods for function minimization
Shanno, D. F. (1970) · Mathematics of Computation · doi:10.1090/s0025-5718-1970-0274029-x · Cited by 3,872
Engineering Optimization Theory and Practice
Rao, S. S. (2019) · Wiley eBooks · doi:10.1002/9781119454816 · Cited by 3,578
An Automatic Method for Finding the Greatest or Least Value of a Function
Rosenbrock, H. H. (1960) · The Computer Journal · doi:10.1093/comjnl/3.3.175 · Cited by 2,957
Storn, R. (1995) · Medical Entomology and Zoology · Cited by 2,800
Firefly algorithm, stochastic test functions and design optimisation
Yang, X. S. (2010) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2010.032124 · Cited by 2,729
Self-citation(2005) · Cited by 2,565
Cited by
A novel nature-inspired algorithm for optimization: Squirrel search algorithm
Jain, M., Singh, V., & Rani, A. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.02.013 · Cited by 961
Rajwar, K., Deep, K., & Das, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10470-y · Cited by 687
Nature-inspired optimization algorithms: Challenges and open problems
Yang, X.-S. (2020) · Journal of Computational Science · doi:10.1016/j.jocs.2020.101104 · Cited by 429
Self-citationA new meta-heuristic optimizer: Pathfinder algorithm
Yapici, H., & Cetinkaya, N. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.012 · Cited by 339
Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23063293 · Cited by 146
Metadata
- Authors
- Jamil, M., & Yang, X. S.
- Venue
- International Journal of Mathematical Modelling and Numerical Optimisation
- Publisher
- Inderscience Publishers
- Published
- 2013
- Volume
- 4
- Issue
- 2
- Pages
- 150
- ISSN
- 2040-3607 (ISSN-L)
- 2040-3615
- OpenAlex
- W2119401655
- Cited by
- 1,301
- References
- 54
- FWCI
- 54.85
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
