CEC2017 Benchmark: Optimal Values and Standard Deviations for 30-Dimensional Functions

Numerical results from 30 runs of 8 optimization algorithms on the 29 test functions of the CEC2017 benchmark suite in 30 dimensions. The dataset contains average optimal values and standard deviations, compiled by Yang Cao and last updated in May 2026. The data is provided in a single 16.7 KB XLSX file.

Use Cases

• Benchmarking algorithm performance based on the reported average optimal values and standard deviations.
• Comparing the convergence reliability of different algorithms based on the results from 30 independent runs.
• Analyzing algorithm behavior across a diverse set of 29 test functions.
• Validating new optimization methods against established algorithms like DE, SaDE, and LSHADE.

Strengths

• Results are aggregated from 30 independent runs per algorithm, providing a measure of statistical reliability.
• Covers 8 distinct optimization algorithms, enabling direct comparison.
• Evaluated on the standardized CEC2017 benchmark suite of 29 test functions.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.
• The dataset is very small (16.7 KB), indicating it contains summary statistics rather than raw run data.

Provenance

Source

Yang Cao via figshare.

Collection Method

Results from computational experiments running algorithms on benchmark functions.

Time Range

The data reflects the state of the CEC2017 benchmark as of the author's experiments.

Freshness

Last updated 2026-05-13 17:44:39; freshness should be verified.

Geography

Global (benchmark functions are not geographically bound).