CEC2017 Benchmark: Optimal Values and Deviations for 10-Dimensional Functions

30 runs of 8 evolutionary algorithms on 29 test functions provide a benchmark for optimization performance. The dataset contains average optimal values and standard deviations for 10-dimensional problems. Authored by Yang Cao and last updated in May 2026, it is shared under a CC-BY-4.0 license.

Use Cases

• Benchmarking algorithm performance based on average optimal values and standard deviations.
• Comparing the stability of optimization runs across different algorithms.
• Validating new optimization methods against established benchmark functions.

Strengths

• Results are aggregated from 30 independent runs per algorithm, providing statistical reliability.
• Compares 8 distinct algorithms, including DE, SaDE, SHADE, ILSHADE, jSO, MPEDE, LSHADE, and RL-DE.
• Covers 29 distinct test functions from the CEC2017 benchmark suite.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (16.4 KB), indicating limited scope.

Provenance

Source

Yang Cao via figshare.

Collection Method

Numerical results from computational experiments.

Freshness

Last updated 2026-05-13 17:44:38; freshness should be verified.