CEC2022 Function Optimization Results for 7 Algorithms

30 independent runs of 7 evolutionary algorithms on 12 benchmark functions provide average optimal values and standard deviations. The dataset, authored by Yang Cao, contains results for 20-dimensional problems. It was last updated on May 13, 2026.

Use Cases

• Benchmarking algorithm performance based on average optimal values and standard deviations
• Comparing the stability of different evolutionary algorithms across multiple runs
• Analyzing the effectiveness of algorithms like SaDE, SHADE, and jSO on high-dimensional problems

Strengths

• Results are aggregated from 30 independent runs per algorithm, providing statistical reliability
• Compares 7 distinct evolutionary algorithms on a common set of 12 test functions

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment

Provenance

Source

Yang Cao via figshare

Collection Method

Numerical results from running optimization algorithms.

Freshness

Last updated 2026-05-13 17:44:43